Thesis on avalanche prediction using One Class SVMLinearly increasing data with manual resetsklearn -...

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Thesis on avalanche prediction using One Class SVM


Linearly increasing data with manual resetsklearn - overfitting problemDate prediction - periodic recurrenceIdentifying Waveform Segments Using Training WaveformsTime series prediction without sliding windowMulti-label classification of text with variable tag distribution in KerasNLP how to go beyond simple intent finding--using context and targeting objectsHow can I improve the accuracy of my neural network on a very unbalanced dataset?Word classification (not text classification) using NLPAnomaly detection on text data using one Class SVM













3












$begingroup$


I'm doing my thesis on avalanche prediction using machine learning.



For my input features i'm using avalanche accidents with features like slope, altitude, facing direction of the slope, combined with according weather data from the day the avalanche occurred.



I want to predict an avalanche when certain variables combine and create a deadly avalanche situation. So 1: the avalanche occurs. 0: The avalanche does not occur. The only data in my database are occured avalanches, i got around 200 samples. So I don't have any data of a non deadly avalanche situation, which is mostly the case.



My question is if a One Class SVM is a good approach to take on this clasification?










share|improve this question







New contributor




Pieter De Malsche is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
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$endgroup$

















    3












    $begingroup$


    I'm doing my thesis on avalanche prediction using machine learning.



    For my input features i'm using avalanche accidents with features like slope, altitude, facing direction of the slope, combined with according weather data from the day the avalanche occurred.



    I want to predict an avalanche when certain variables combine and create a deadly avalanche situation. So 1: the avalanche occurs. 0: The avalanche does not occur. The only data in my database are occured avalanches, i got around 200 samples. So I don't have any data of a non deadly avalanche situation, which is mostly the case.



    My question is if a One Class SVM is a good approach to take on this clasification?










    share|improve this question







    New contributor




    Pieter De Malsche is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
    Check out our Code of Conduct.







    $endgroup$















      3












      3








      3





      $begingroup$


      I'm doing my thesis on avalanche prediction using machine learning.



      For my input features i'm using avalanche accidents with features like slope, altitude, facing direction of the slope, combined with according weather data from the day the avalanche occurred.



      I want to predict an avalanche when certain variables combine and create a deadly avalanche situation. So 1: the avalanche occurs. 0: The avalanche does not occur. The only data in my database are occured avalanches, i got around 200 samples. So I don't have any data of a non deadly avalanche situation, which is mostly the case.



      My question is if a One Class SVM is a good approach to take on this clasification?










      share|improve this question







      New contributor




      Pieter De Malsche is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
      Check out our Code of Conduct.







      $endgroup$




      I'm doing my thesis on avalanche prediction using machine learning.



      For my input features i'm using avalanche accidents with features like slope, altitude, facing direction of the slope, combined with according weather data from the day the avalanche occurred.



      I want to predict an avalanche when certain variables combine and create a deadly avalanche situation. So 1: the avalanche occurs. 0: The avalanche does not occur. The only data in my database are occured avalanches, i got around 200 samples. So I don't have any data of a non deadly avalanche situation, which is mostly the case.



      My question is if a One Class SVM is a good approach to take on this clasification?







      machine-learning python






      share|improve this question







      New contributor




      Pieter De Malsche is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
      Check out our Code of Conduct.











      share|improve this question







      New contributor




      Pieter De Malsche is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
      Check out our Code of Conduct.









      share|improve this question




      share|improve this question






      New contributor




      Pieter De Malsche is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
      Check out our Code of Conduct.









      asked 20 hours ago









      Pieter De MalschePieter De Malsche

      161




      161




      New contributor




      Pieter De Malsche is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
      Check out our Code of Conduct.





      New contributor





      Pieter De Malsche is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
      Check out our Code of Conduct.






      Pieter De Malsche is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
      Check out our Code of Conduct.






















          3 Answers
          3






          active

          oldest

          votes


















          4












          $begingroup$

          Your problem seems to belong to novelty detection in the general area of OCC problems.



          So, the short answer is: yes. You can apply SVDD (Support Vector Data Description) method to get the smallest hypersphere containing samples in the dataset and then assess whether a new observation is an outlier or not.



          Of course, the less representative your dataset is, the less accurate your classifier will be.






          share|improve this answer









          $endgroup$





















            2












            $begingroup$

            You can use a method of data mining to predict avalanches, however, there are some pit falls which I can provide you based on my basic avalanche knowledge from mountaineering.




            1. What do you want to predict? Spontanous avalanches (mainly threating villages and roads) or human triggered avalanches (mainly affecting skiers). The factors for these are completely different

            2. Getting data has already been mentioned. There are some data sets related to avalanche incidents, for example at the swiss avlanche research institute: https://www.slf.ch/de/lawinen/unfaelle-und-schadenlawinen/alle-gemeldeten-lawinenunfaelle-aktuell.html
              However, there is naturally little data about cases where no avalanche was triggered and where an avalanche was triggered but nobody harmed and therefore not reported. There have been some tries to estimate the number of people on tour based on touring reports in the internet.

            3. Getting precise data is even more of a problem. Consider figure 2 in this weeks report: https://www.slf.ch/de/lawinenbulletin-und-schneesituation/wochen-und-winterberichte/201819/wob-18-25-april.html
              It compares the same slope at a time difference of 45 minutes and it looks completely different.

            4. Feature selection is another big issue. You mention that you want to use weather data from the day of the incident. I think this is drawing the wrong conclusions as most skiing avalanches happen during the weekends and probably in slightly better weather. Also most people will be sensible and not go ski touring on risky tours on risky days. This has a big potential to skew your data and your model






            share|improve this answer








            New contributor




            Manziel is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
            Check out our Code of Conduct.






            $endgroup$





















              2












              $begingroup$

              Could you look for any possible way to get non-avalanche data?



              1) Avalanches happened in a mountains chain. Could you add to your data neighboring peaks data from the same day avalanche happened?



              2) You may have good insights from data exploration. For instance, what is the minimal slope that mountain should have to be able to produce avalanches? Range of temperatures?



              3) Could you look for other data sets (with non-avalanches entries) that you could combine with your data?






              share|improve this answer











              $endgroup$









              • 1




                $begingroup$
                it is incorrect that there is no way to learn a classification model if the training data does not contain at least two classes. The field which addresses this is known as "one-class classification" (or anomaly detection, or outlier detection, it probably also has other names). In this field the broad approach is to build a model of the data and define a distance measure and threshold. New data points which are closer than the threshold are labelled as the training data and those which are further are labelled as the other class. Hope this helps.
                $endgroup$
                – cfogelberg
                10 hours ago












              • $begingroup$
                Thanks for the comment and explanation. I didn't know that the outlier detection can also be treated as classification. It is a very interesting point of view. I have edited reply to not mislead others.
                $endgroup$
                – Tatyana
                8 hours ago














              Your Answer








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              3 Answers
              3






              active

              oldest

              votes








              3 Answers
              3






              active

              oldest

              votes









              active

              oldest

              votes






              active

              oldest

              votes









              4












              $begingroup$

              Your problem seems to belong to novelty detection in the general area of OCC problems.



              So, the short answer is: yes. You can apply SVDD (Support Vector Data Description) method to get the smallest hypersphere containing samples in the dataset and then assess whether a new observation is an outlier or not.



              Of course, the less representative your dataset is, the less accurate your classifier will be.






              share|improve this answer









              $endgroup$


















                4












                $begingroup$

                Your problem seems to belong to novelty detection in the general area of OCC problems.



                So, the short answer is: yes. You can apply SVDD (Support Vector Data Description) method to get the smallest hypersphere containing samples in the dataset and then assess whether a new observation is an outlier or not.



                Of course, the less representative your dataset is, the less accurate your classifier will be.






                share|improve this answer









                $endgroup$
















                  4












                  4








                  4





                  $begingroup$

                  Your problem seems to belong to novelty detection in the general area of OCC problems.



                  So, the short answer is: yes. You can apply SVDD (Support Vector Data Description) method to get the smallest hypersphere containing samples in the dataset and then assess whether a new observation is an outlier or not.



                  Of course, the less representative your dataset is, the less accurate your classifier will be.






                  share|improve this answer









                  $endgroup$



                  Your problem seems to belong to novelty detection in the general area of OCC problems.



                  So, the short answer is: yes. You can apply SVDD (Support Vector Data Description) method to get the smallest hypersphere containing samples in the dataset and then assess whether a new observation is an outlier or not.



                  Of course, the less representative your dataset is, the less accurate your classifier will be.







                  share|improve this answer












                  share|improve this answer



                  share|improve this answer










                  answered 18 hours ago









                  sentencesentence

                  1734




                  1734























                      2












                      $begingroup$

                      You can use a method of data mining to predict avalanches, however, there are some pit falls which I can provide you based on my basic avalanche knowledge from mountaineering.




                      1. What do you want to predict? Spontanous avalanches (mainly threating villages and roads) or human triggered avalanches (mainly affecting skiers). The factors for these are completely different

                      2. Getting data has already been mentioned. There are some data sets related to avalanche incidents, for example at the swiss avlanche research institute: https://www.slf.ch/de/lawinen/unfaelle-und-schadenlawinen/alle-gemeldeten-lawinenunfaelle-aktuell.html
                        However, there is naturally little data about cases where no avalanche was triggered and where an avalanche was triggered but nobody harmed and therefore not reported. There have been some tries to estimate the number of people on tour based on touring reports in the internet.

                      3. Getting precise data is even more of a problem. Consider figure 2 in this weeks report: https://www.slf.ch/de/lawinenbulletin-und-schneesituation/wochen-und-winterberichte/201819/wob-18-25-april.html
                        It compares the same slope at a time difference of 45 minutes and it looks completely different.

                      4. Feature selection is another big issue. You mention that you want to use weather data from the day of the incident. I think this is drawing the wrong conclusions as most skiing avalanches happen during the weekends and probably in slightly better weather. Also most people will be sensible and not go ski touring on risky tours on risky days. This has a big potential to skew your data and your model






                      share|improve this answer








                      New contributor




                      Manziel is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
                      Check out our Code of Conduct.






                      $endgroup$


















                        2












                        $begingroup$

                        You can use a method of data mining to predict avalanches, however, there are some pit falls which I can provide you based on my basic avalanche knowledge from mountaineering.




                        1. What do you want to predict? Spontanous avalanches (mainly threating villages and roads) or human triggered avalanches (mainly affecting skiers). The factors for these are completely different

                        2. Getting data has already been mentioned. There are some data sets related to avalanche incidents, for example at the swiss avlanche research institute: https://www.slf.ch/de/lawinen/unfaelle-und-schadenlawinen/alle-gemeldeten-lawinenunfaelle-aktuell.html
                          However, there is naturally little data about cases where no avalanche was triggered and where an avalanche was triggered but nobody harmed and therefore not reported. There have been some tries to estimate the number of people on tour based on touring reports in the internet.

                        3. Getting precise data is even more of a problem. Consider figure 2 in this weeks report: https://www.slf.ch/de/lawinenbulletin-und-schneesituation/wochen-und-winterberichte/201819/wob-18-25-april.html
                          It compares the same slope at a time difference of 45 minutes and it looks completely different.

                        4. Feature selection is another big issue. You mention that you want to use weather data from the day of the incident. I think this is drawing the wrong conclusions as most skiing avalanches happen during the weekends and probably in slightly better weather. Also most people will be sensible and not go ski touring on risky tours on risky days. This has a big potential to skew your data and your model






                        share|improve this answer








                        New contributor




                        Manziel is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
                        Check out our Code of Conduct.






                        $endgroup$
















                          2












                          2








                          2





                          $begingroup$

                          You can use a method of data mining to predict avalanches, however, there are some pit falls which I can provide you based on my basic avalanche knowledge from mountaineering.




                          1. What do you want to predict? Spontanous avalanches (mainly threating villages and roads) or human triggered avalanches (mainly affecting skiers). The factors for these are completely different

                          2. Getting data has already been mentioned. There are some data sets related to avalanche incidents, for example at the swiss avlanche research institute: https://www.slf.ch/de/lawinen/unfaelle-und-schadenlawinen/alle-gemeldeten-lawinenunfaelle-aktuell.html
                            However, there is naturally little data about cases where no avalanche was triggered and where an avalanche was triggered but nobody harmed and therefore not reported. There have been some tries to estimate the number of people on tour based on touring reports in the internet.

                          3. Getting precise data is even more of a problem. Consider figure 2 in this weeks report: https://www.slf.ch/de/lawinenbulletin-und-schneesituation/wochen-und-winterberichte/201819/wob-18-25-april.html
                            It compares the same slope at a time difference of 45 minutes and it looks completely different.

                          4. Feature selection is another big issue. You mention that you want to use weather data from the day of the incident. I think this is drawing the wrong conclusions as most skiing avalanches happen during the weekends and probably in slightly better weather. Also most people will be sensible and not go ski touring on risky tours on risky days. This has a big potential to skew your data and your model






                          share|improve this answer








                          New contributor




                          Manziel is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
                          Check out our Code of Conduct.






                          $endgroup$



                          You can use a method of data mining to predict avalanches, however, there are some pit falls which I can provide you based on my basic avalanche knowledge from mountaineering.




                          1. What do you want to predict? Spontanous avalanches (mainly threating villages and roads) or human triggered avalanches (mainly affecting skiers). The factors for these are completely different

                          2. Getting data has already been mentioned. There are some data sets related to avalanche incidents, for example at the swiss avlanche research institute: https://www.slf.ch/de/lawinen/unfaelle-und-schadenlawinen/alle-gemeldeten-lawinenunfaelle-aktuell.html
                            However, there is naturally little data about cases where no avalanche was triggered and where an avalanche was triggered but nobody harmed and therefore not reported. There have been some tries to estimate the number of people on tour based on touring reports in the internet.

                          3. Getting precise data is even more of a problem. Consider figure 2 in this weeks report: https://www.slf.ch/de/lawinenbulletin-und-schneesituation/wochen-und-winterberichte/201819/wob-18-25-april.html
                            It compares the same slope at a time difference of 45 minutes and it looks completely different.

                          4. Feature selection is another big issue. You mention that you want to use weather data from the day of the incident. I think this is drawing the wrong conclusions as most skiing avalanches happen during the weekends and probably in slightly better weather. Also most people will be sensible and not go ski touring on risky tours on risky days. This has a big potential to skew your data and your model







                          share|improve this answer








                          New contributor




                          Manziel is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
                          Check out our Code of Conduct.









                          share|improve this answer



                          share|improve this answer






                          New contributor




                          Manziel is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
                          Check out our Code of Conduct.









                          answered 13 hours ago









                          ManzielManziel

                          1212




                          1212




                          New contributor




                          Manziel is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
                          Check out our Code of Conduct.





                          New contributor





                          Manziel is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
                          Check out our Code of Conduct.






                          Manziel is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
                          Check out our Code of Conduct.























                              2












                              $begingroup$

                              Could you look for any possible way to get non-avalanche data?



                              1) Avalanches happened in a mountains chain. Could you add to your data neighboring peaks data from the same day avalanche happened?



                              2) You may have good insights from data exploration. For instance, what is the minimal slope that mountain should have to be able to produce avalanches? Range of temperatures?



                              3) Could you look for other data sets (with non-avalanches entries) that you could combine with your data?






                              share|improve this answer











                              $endgroup$









                              • 1




                                $begingroup$
                                it is incorrect that there is no way to learn a classification model if the training data does not contain at least two classes. The field which addresses this is known as "one-class classification" (or anomaly detection, or outlier detection, it probably also has other names). In this field the broad approach is to build a model of the data and define a distance measure and threshold. New data points which are closer than the threshold are labelled as the training data and those which are further are labelled as the other class. Hope this helps.
                                $endgroup$
                                – cfogelberg
                                10 hours ago












                              • $begingroup$
                                Thanks for the comment and explanation. I didn't know that the outlier detection can also be treated as classification. It is a very interesting point of view. I have edited reply to not mislead others.
                                $endgroup$
                                – Tatyana
                                8 hours ago


















                              2












                              $begingroup$

                              Could you look for any possible way to get non-avalanche data?



                              1) Avalanches happened in a mountains chain. Could you add to your data neighboring peaks data from the same day avalanche happened?



                              2) You may have good insights from data exploration. For instance, what is the minimal slope that mountain should have to be able to produce avalanches? Range of temperatures?



                              3) Could you look for other data sets (with non-avalanches entries) that you could combine with your data?






                              share|improve this answer











                              $endgroup$









                              • 1




                                $begingroup$
                                it is incorrect that there is no way to learn a classification model if the training data does not contain at least two classes. The field which addresses this is known as "one-class classification" (or anomaly detection, or outlier detection, it probably also has other names). In this field the broad approach is to build a model of the data and define a distance measure and threshold. New data points which are closer than the threshold are labelled as the training data and those which are further are labelled as the other class. Hope this helps.
                                $endgroup$
                                – cfogelberg
                                10 hours ago












                              • $begingroup$
                                Thanks for the comment and explanation. I didn't know that the outlier detection can also be treated as classification. It is a very interesting point of view. I have edited reply to not mislead others.
                                $endgroup$
                                – Tatyana
                                8 hours ago
















                              2












                              2








                              2





                              $begingroup$

                              Could you look for any possible way to get non-avalanche data?



                              1) Avalanches happened in a mountains chain. Could you add to your data neighboring peaks data from the same day avalanche happened?



                              2) You may have good insights from data exploration. For instance, what is the minimal slope that mountain should have to be able to produce avalanches? Range of temperatures?



                              3) Could you look for other data sets (with non-avalanches entries) that you could combine with your data?






                              share|improve this answer











                              $endgroup$



                              Could you look for any possible way to get non-avalanche data?



                              1) Avalanches happened in a mountains chain. Could you add to your data neighboring peaks data from the same day avalanche happened?



                              2) You may have good insights from data exploration. For instance, what is the minimal slope that mountain should have to be able to produce avalanches? Range of temperatures?



                              3) Could you look for other data sets (with non-avalanches entries) that you could combine with your data?







                              share|improve this answer














                              share|improve this answer



                              share|improve this answer








                              edited 8 hours ago

























                              answered 18 hours ago









                              TatyanaTatyana

                              414




                              414








                              • 1




                                $begingroup$
                                it is incorrect that there is no way to learn a classification model if the training data does not contain at least two classes. The field which addresses this is known as "one-class classification" (or anomaly detection, or outlier detection, it probably also has other names). In this field the broad approach is to build a model of the data and define a distance measure and threshold. New data points which are closer than the threshold are labelled as the training data and those which are further are labelled as the other class. Hope this helps.
                                $endgroup$
                                – cfogelberg
                                10 hours ago












                              • $begingroup$
                                Thanks for the comment and explanation. I didn't know that the outlier detection can also be treated as classification. It is a very interesting point of view. I have edited reply to not mislead others.
                                $endgroup$
                                – Tatyana
                                8 hours ago
















                              • 1




                                $begingroup$
                                it is incorrect that there is no way to learn a classification model if the training data does not contain at least two classes. The field which addresses this is known as "one-class classification" (or anomaly detection, or outlier detection, it probably also has other names). In this field the broad approach is to build a model of the data and define a distance measure and threshold. New data points which are closer than the threshold are labelled as the training data and those which are further are labelled as the other class. Hope this helps.
                                $endgroup$
                                – cfogelberg
                                10 hours ago












                              • $begingroup$
                                Thanks for the comment and explanation. I didn't know that the outlier detection can also be treated as classification. It is a very interesting point of view. I have edited reply to not mislead others.
                                $endgroup$
                                – Tatyana
                                8 hours ago










                              1




                              1




                              $begingroup$
                              it is incorrect that there is no way to learn a classification model if the training data does not contain at least two classes. The field which addresses this is known as "one-class classification" (or anomaly detection, or outlier detection, it probably also has other names). In this field the broad approach is to build a model of the data and define a distance measure and threshold. New data points which are closer than the threshold are labelled as the training data and those which are further are labelled as the other class. Hope this helps.
                              $endgroup$
                              – cfogelberg
                              10 hours ago






                              $begingroup$
                              it is incorrect that there is no way to learn a classification model if the training data does not contain at least two classes. The field which addresses this is known as "one-class classification" (or anomaly detection, or outlier detection, it probably also has other names). In this field the broad approach is to build a model of the data and define a distance measure and threshold. New data points which are closer than the threshold are labelled as the training data and those which are further are labelled as the other class. Hope this helps.
                              $endgroup$
                              – cfogelberg
                              10 hours ago














                              $begingroup$
                              Thanks for the comment and explanation. I didn't know that the outlier detection can also be treated as classification. It is a very interesting point of view. I have edited reply to not mislead others.
                              $endgroup$
                              – Tatyana
                              8 hours ago






                              $begingroup$
                              Thanks for the comment and explanation. I didn't know that the outlier detection can also be treated as classification. It is a very interesting point of view. I have edited reply to not mislead others.
                              $endgroup$
                              – Tatyana
                              8 hours ago












                              Pieter De Malsche is a new contributor. Be nice, and check out our Code of Conduct.










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