{"action":"created","issue":{"url":"https://api.github.com/repos/scikit-learn/scikit-learn/issues/2606","repository_url":"https://api.github.com/repos/scikit-learn/scikit-learn","labels_url":"https://api.github.com/repos/scikit-learn/scikit-learn/issues/2606/labels{/name}","comments_url":"https://api.github.com/repos/scikit-learn/scikit-learn/issues/2606/comments","events_url":"https://api.github.com/repos/scikit-learn/scikit-learn/issues/2606/events","html_url":"https://github.com/scikit-learn/scikit-learn/issues/2606","id":23115369,"number":2606,"title":"Add support for: ML-kNN","user":{"login":"mchangun","id":4102015,"avatar_url":"https://avatars.githubusercontent.com/u/4102015?v=3","gravatar_id":"","url":"https://api.github.com/users/mchangun","html_url":"https://github.com/mchangun","followers_url":"https://api.github.com/users/mchangun/followers","following_url":"https://api.github.com/users/mchangun/following{/other_user}","gists_url":"https://api.github.com/users/mchangun/gists{/gist_id}","starred_url":"https://api.github.com/users/mchangun/starred{/owner}{/repo}","subscriptions_url":"https://api.github.com/users/mchangun/subscriptions","organizations_url":"https://api.github.com/users/mchangun/orgs","repos_url":"https://api.github.com/users/mchangun/repos","events_url":"https://api.github.com/users/mchangun/events{/privacy}","received_events_url":"https://api.github.com/users/mchangun/received_events","type":"User","site_admin":false},"labels":[{"url":"https://api.github.com/repos/scikit-learn/scikit-learn/labels/Enhancement","name":"Enhancement","color":"d7e102"}],"state":"open","locked":false,"assignee":null,"milestone":null,"comments":7,"created_at":"2013-11-22T03:19:04Z","updated_at":"2016-03-20T16:13:13Z","closed_at":null,"body":"Add support for the Multi-Label kNN algorithm as described here in this paper http://cs.nju.edu.cn/zhouzh/zhouzh.files/publication/pr07.pdf. A brief description of the algorithm from the above paper:\r\n\r\n>\"As its name implied, Ml-knn is derived from the popular k-Nearest Neighbor (kNN) algorithm [1]. Firstly, for each test instance, its k nearest neighbors in the training set are identi¯ed. Then, according to statistical information gained from the label sets of these neighboring instances, i.e. the number of neighboring instances belonging to each possible class, maximum a posteriori (MAP) principle is utilized to determine the label set for the test instance.\"\r\n\r\nSupporting sparse matrices should be a key requirement - a lot of multi label tasks are for text categorization and these are usually represented as sparse matrices.\r\n\r\nML-kNN is actually already implemented in this library (http://orange.biolab.si/docs/latest/reference/rst/Orange.multilabel/#ml-knn-learner) but it would be good to bring it under the scikit-learn framework as well.\r\n\r\n"},"comment":{"url":"https://api.github.com/repos/scikit-learn/scikit-learn/issues/comments/198955995","html_url":"https://github.com/scikit-learn/scikit-learn/issues/2606#issuecomment-198955995","issue_url":"https://api.github.com/repos/scikit-learn/scikit-learn/issues/2606","id":198955995,"user":{"login":"medhini","id":7105011,"avatar_url":"https://avatars.githubusercontent.com/u/7105011?v=3","gravatar_id":"","url":"https://api.github.com/users/medhini","html_url":"https://github.com/medhini","followers_url":"https://api.github.com/users/medhini/followers","following_url":"https://api.github.com/users/medhini/following{/other_user}","gists_url":"https://api.github.com/users/medhini/gists{/gist_id}","starred_url":"https://api.github.com/users/medhini/starred{/owner}{/repo}","subscriptions_url":"https://api.github.com/users/medhini/subscriptions","organizations_url":"https://api.github.com/users/medhini/orgs","repos_url":"https://api.github.com/users/medhini/repos","events_url":"https://api.github.com/users/medhini/events{/privacy}","received_events_url":"https://api.github.com/users/medhini/received_events","type":"User","site_admin":false},"created_at":"2016-03-20T16:13:13Z","updated_at":"2016-03-20T16:13:13Z","body":"Has this been implemented already? Is the issue still open?"}}