{"action":"created","issue":{"url":"https://api.github.com/repos/geneura-papers/2015-CIDS/issues/1","labels_url":"https://api.github.com/repos/geneura-papers/2015-CIDS/issues/1/labels{/name}","comments_url":"https://api.github.com/repos/geneura-papers/2015-CIDS/issues/1/comments","events_url":"https://api.github.com/repos/geneura-papers/2015-CIDS/issues/1/events","html_url":"https://github.com/geneura-papers/2015-CIDS/issues/1","id":58042766,"number":1,"title":"Apply Feature Selection to the URL data used in ECTA","user":{"login":"amorag","id":3063386,"avatar_url":"https://avatars.githubusercontent.com/u/3063386?v=3","gravatar_id":"","url":"https://api.github.com/users/amorag","html_url":"https://github.com/amorag","followers_url":"https://api.github.com/users/amorag/followers","following_url":"https://api.github.com/users/amorag/following{/other_user}","gists_url":"https://api.github.com/users/amorag/gists{/gist_id}","starred_url":"https://api.github.com/users/amorag/starred{/owner}{/repo}","subscriptions_url":"https://api.github.com/users/amorag/subscriptions","organizations_url":"https://api.github.com/users/amorag/orgs","repos_url":"https://api.github.com/users/amorag/repos","events_url":"https://api.github.com/users/amorag/events{/privacy}","received_events_url":"https://api.github.com/users/amorag/received_events","type":"User","site_admin":false},"labels":[],"state":"open","locked":false,"assignee":{"login":"unintendedbear","id":3061254,"avatar_url":"https://avatars.githubusercontent.com/u/3061254?v=3","gravatar_id":"","url":"https://api.github.com/users/unintendedbear","html_url":"https://github.com/unintendedbear","followers_url":"https://api.github.com/users/unintendedbear/followers","following_url":"https://api.github.com/users/unintendedbear/following{/other_user}","gists_url":"https://api.github.com/users/unintendedbear/gists{/gist_id}","starred_url":"https://api.github.com/users/unintendedbear/starred{/owner}{/repo}","subscriptions_url":"https://api.github.com/users/unintendedbear/subscriptions","organizations_url":"https://api.github.com/users/unintendedbear/orgs","repos_url":"https://api.github.com/users/unintendedbear/repos","events_url":"https://api.github.com/users/unintendedbear/events{/privacy}","received_events_url":"https://api.github.com/users/unintendedbear/received_events","type":"User","site_admin":false},"milestone":{"url":"https://api.github.com/repos/geneura-papers/2015-CIDS/milestones/1","labels_url":"https://api.github.com/repos/geneura-papers/2015-CIDS/milestones/1/labels","id":984159,"number":1,"title":"Experimental results","description":"The new experiments on URLs using Feature Selection methods","creator":{"login":"amorag","id":3063386,"avatar_url":"https://avatars.githubusercontent.com/u/3063386?v=3","gravatar_id":"","url":"https://api.github.com/users/amorag","html_url":"https://github.com/amorag","followers_url":"https://api.github.com/users/amorag/followers","following_url":"https://api.github.com/users/amorag/following{/other_user}","gists_url":"https://api.github.com/users/amorag/gists{/gist_id}","starred_url":"https://api.github.com/users/amorag/starred{/owner}{/repo}","subscriptions_url":"https://api.github.com/users/amorag/subscriptions","organizations_url":"https://api.github.com/users/amorag/orgs","repos_url":"https://api.github.com/users/amorag/repos","events_url":"https://api.github.com/users/amorag/events{/privacy}","received_events_url":"https://api.github.com/users/amorag/received_events","type":"User","site_admin":false},"open_issues":2,"closed_issues":0,"state":"open","created_at":"2015-02-18T09:03:00Z","updated_at":"2015-02-18T12:17:15Z","due_on":"2015-02-22T23:00:00Z","closed_at":null},"comments":10,"created_at":"2015-02-18T09:06:56Z","updated_at":"2015-02-19T08:54:41Z","closed_at":null,"body":"And create two tables with the results:\r\n1) Selected Features\r\n2) Classification Accuracies per method with the new features over every dataset (similar table to the existing one)"},"comment":{"url":"https://api.github.com/repos/geneura-papers/2015-CIDS/issues/comments/75016800","html_url":"https://github.com/geneura-papers/2015-CIDS/issues/1#issuecomment-75016800","issue_url":"https://api.github.com/repos/geneura-papers/2015-CIDS/issues/1","id":75016800,"user":{"login":"amorag","id":3063386,"avatar_url":"https://avatars.githubusercontent.com/u/3063386?v=3","gravatar_id":"","url":"https://api.github.com/users/amorag","html_url":"https://github.com/amorag","followers_url":"https://api.github.com/users/amorag/followers","following_url":"https://api.github.com/users/amorag/following{/other_user}","gists_url":"https://api.github.com/users/amorag/gists{/gist_id}","starred_url":"https://api.github.com/users/amorag/starred{/owner}{/repo}","subscriptions_url":"https://api.github.com/users/amorag/subscriptions","organizations_url":"https://api.github.com/users/amorag/orgs","repos_url":"https://api.github.com/users/amorag/repos","events_url":"https://api.github.com/users/amorag/events{/privacy}","received_events_url":"https://api.github.com/users/amorag/received_events","type":"User","site_admin":false},"created_at":"2015-02-19T08:54:41Z","updated_at":"2015-02-19T08:54:41Z","body":"Dear all, some clarificaton from my point of view.\r\n\r\nThe aim was to extend the ECTA paper (which classified URLs data using Rule-based and Tree-based methods).\r\nThis would be done using a Feature Selection process (by Zeineb).\r\n\r\nJJ, we are not testing all the methods, but the chosen 5 (the best), as Paloma said.\r\nMoreover, we have reduced the experiments to 1/3rd.\r\nThe objective is to test how a feature selection process could improve the obtained results, and which features could be removed without influence in the classification accuracies.\r\n\r\nZeineb, Paloma did a set of experiments considering unbalanced data, and also balanced data applying both over- and undersampling methods. She created some distributions on training/test files, namely 80-20% and 90-10%.\r\nThen, inside every subset, we considered a sequential distribution of the patterns (they are sorted in time), and also a random distribution.\r\nIn this random distribution, she generated three different pairs of files, in order to obtain the average accuracy.\r\n\r\nIn this paper I'm proposing to consider the best set of results (those obtained for unbalanced data) and apply the feature selection.\r\nThis method just can be applied on the global unbalanced dataset in order to choose the best features.\r\n\r\nAfter this, the classification process should be repeated for the same methods and with the same distribution. However, as we tested yesterday, the datasets must not be generated again. We can use the same as we have, and choose in Weka which features to consider or to remove in the preprocessing menu, so once a dataset is loaded. Then apply the 5 methods to that dataset.\r\n\r\nI think this proposal should be the minimum to do in order to reach some well-founded conclusions, since we can check which techniques get more profit of the feature reduction, if this improves the classification accuracies, and so on.\r\n\r\nLet me know your opinion.\r\n\r\nCheers,\r\nAntonio"}}