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Feature selection methods provides us a way of reducing computation time, improving prediction performance, and a better understanding of the data in machine learning or pattern recognition applications. In this paper we provide an overview of some of the methods present in literature. The objective is to provide a generic introduction to variable elimination which can be applied to a wide array of machine learning problems. We focus on Filter, Wrapper and Embedded methods. We also apply some of the feature selection techniques on standard datasets to demonstrate the applicability of feature selection techniques.", "publicationTitle": "Computers & Electrical Engineering", "publisher": "", "place": "", "date": "January 2014", "volume": "40", "issue": "1", "section": "", "partNumber": "", "partTitle": "", "pages": "16-28", "series": "", "seriesTitle": "", "seriesText": "", "journalAbbreviation": "Computers & Electrical Engineering", "DOI": "10.1016/j.compeleceng.2013.11.024", "citationKey": "", "url": "http://www.sciencedirect.com/science/article/pii/S0045790613003066", "accessDate": "2014-02-20T10:36:57Z", "PMID": "", "PMCID": "", "ISSN": "0045-7906", "archive": "", "archiveLocation": "", "shortTitle": "", "language": "", "libraryCatalog": "ScienceDirect", "callNumber": "", "rights": "", "extra": "", "tags": [], "collections": [], "relations": {}, "dateAdded": "2014-02-20T10:36:57Z", "dateModified": "2014-02-20T10:36:57Z" } }, { "key": "2D9BFP9D", "version": 685, "library": { "type": "group", "id": 23835, "name": "Feature Selection", "links": { "alternate": { "href": "https://www.zotero.org/groups/feature_selection", "type": "text/html" } } }, "links": { "self": { "href": "https://api.zotero.org/groups/23835/items/2D9BFP9D", "type": "application/json" }, "alternate": { "href": "https://www.zotero.org/groups/feature_selection/items/2D9BFP9D", "type": "text/html" }, "up": { "href": "https://api.zotero.org/groups/23835/items/R2FRTV29", "type": "application/json" } }, "meta": { "createdByUser": { "id": 185063, "username": "arauzo", "name": "Antonio Arauzo-Azofra", "links": { "alternate": { "href": "https://www.zotero.org/arauzo", "type": "text/html" } } }, "numChildren": 0 }, "data": { "key": "2D9BFP9D", "version": 685, "parentItem": "R2FRTV29", "itemType": "attachment", "linkMode": "imported_file", "title": "Bermejo2014speeding_up_incremental_wrapper_feature_subset_selection_with_naive_bayes.pdf", "accessDate": "", "url": "", "note": "", "contentType": "application/pdf", "charset": "", "filename": "Bermejo2014speeding_up_incremental_wrapper_feature_subset_selection_with_naive_bayes.pdf", "md5": "2a9aa8c3cbe56cb837a5374d888e70b1", "mtime": 1391550582000, "tags": [], "relations": {}, "dateAdded": "2014-02-04T21:49:42Z", "dateModified": "2014-02-04T21:49:42Z" } }, { "key": "R2FRTV29", "version": 682, "library": { "type": "group", "id": 23835, "name": "Feature Selection", "links": { "alternate": { "href": "https://www.zotero.org/groups/feature_selection", "type": "text/html" } } }, "links": { "self": { "href": "https://api.zotero.org/groups/23835/items/R2FRTV29", "type": "application/json" }, "alternate": { "href": "https://www.zotero.org/groups/feature_selection/items/R2FRTV29", "type": "text/html" } }, "meta": { "createdByUser": { "id": 185063, "username": "arauzo", "name": "Antonio Arauzo-Azofra", "links": { "alternate": { "href": "https://www.zotero.org/arauzo", "type": "text/html" } } }, "creatorSummary": "Bermejo et al.", "parsedDate": "2014-01", "numChildren": 3 }, "data": { "key": "R2FRTV29", "version": 682, "itemType": "journalArticle", "title": "Speeding up incremental wrapper feature subset selection with Naive Bayes classifier", "creators": [ { "creatorType": "author", "firstName": "Pablo", "lastName": "Bermejo" }, { "creatorType": "author", "firstName": "José A.", "lastName": "Gámez" }, { "creatorType": "author", "firstName": "José M.", "lastName": "Puerta" } ], "abstractNote": "This paper deals with the problem of wrapper feature subset selection (FSS) in classification-oriented datasets with a (very) large number of attributes. In high-dimensional datasets with thousands of variables, wrapper FSS becomes a laborious computational process because of the amount of CPU time it requires. In this paper we study how under certain circumstances the wrapper FSS process can be speeded up by embedding the classifier into the wrapper algorithm, instead of dealing with it as a black-box. Our proposal is based on the combination of the NB classifier (which is known to be largely beneficial for FSS) with incremental wrapper FSS algorithms. The merit of this approach is analyzed both theoretically and experimentally, and the results show an impressive speed-up for the embedded FSS process.", "publicationTitle": "Knowledge-Based Systems", "publisher": "", "place": "", "date": "January 2014", "volume": "55", "issue": "", "section": "", "partNumber": "", "partTitle": "", "pages": "140-147", "series": "", "seriesTitle": "", "seriesText": "", "journalAbbreviation": "Knowledge-Based Systems", "DOI": "10.1016/j.knosys.2013.10.016", "citationKey": "", "url": "http://www.sciencedirect.com/science/article/pii/S0950705113003274", "accessDate": "2014-02-04T21:46:44Z", "PMID": "", "PMCID": "", "ISSN": "0950-7051", "archive": "", "archiveLocation": "", "shortTitle": "", "language": "", "libraryCatalog": "ScienceDirect", "callNumber": "", "rights": "", "extra": "", "tags": [ { "tag": "High-dimensional data", "type": 1 }, { "tag": "Incremental algorithms", "type": 1 }, { "tag": "Naive Bayes", "type": 1 }, { "tag": "Wrapper feature subset selection", "type": 1 } ], "collections": [], "relations": {}, "dateAdded": "2014-02-04T21:46:44Z", "dateModified": "2014-02-04T21:46:44Z" } }, { "key": "6E6HUNM9", "version": 681, "library": { "type": "group", "id": 23835, "name": "Feature Selection", "links": { "alternate": { "href": "https://www.zotero.org/groups/feature_selection", "type": "text/html" } } }, "links": { "self": { "href": "https://api.zotero.org/groups/23835/items/6E6HUNM9", "type": "application/json" }, "alternate": { "href": "https://www.zotero.org/groups/feature_selection/items/6E6HUNM9", "type": "text/html" }, "up": { "href": "https://api.zotero.org/groups/23835/items/S96TD25D", "type": "application/json" } }, "meta": { "createdByUser": { "id": 185063, "username": "arauzo", "name": "Antonio Arauzo-Azofra", "links": { "alternate": { "href": "https://www.zotero.org/arauzo", "type": "text/html" } } }, "numChildren": 0 }, "data": { "key": "6E6HUNM9", "version": 681, "parentItem": "S96TD25D", "itemType": "attachment", "linkMode": "imported_file", "title": "Shin2009consistency.pdf", "accessDate": "", "url": "", "note": "", "contentType": "application/pdf", "charset": "", "filename": "Shin2009consistency.pdf", "md5": "00285f0d698d494fe99396b33b710923", "mtime": 1389779931000, "tags": [], "relations": {}, "dateAdded": "2014-01-15T09:58:51Z", "dateModified": "2014-01-15T09:58:51Z" } }, { "key": "S96TD25D", "version": 677, "library": { "type": "group", "id": 23835, "name": "Feature Selection", "links": { "alternate": { "href": "https://www.zotero.org/groups/feature_selection", "type": "text/html" } } }, "links": { "self": { "href": "https://api.zotero.org/groups/23835/items/S96TD25D", "type": "application/json" }, "alternate": { "href": "https://www.zotero.org/groups/feature_selection/items/S96TD25D", "type": "text/html" } }, "meta": { "createdByUser": { "id": 185063, "username": "arauzo", "name": "Antonio Arauzo-Azofra", "links": { "alternate": { "href": "https://www.zotero.org/arauzo", "type": "text/html" } } }, "creatorSummary": "Shin and Xu", "parsedDate": "2009-01-01", "numChildren": 3 }, "data": { "key": "S96TD25D", "version": 677, "itemType": "bookSection", "title": "Consistency-Based Feature Selection", "creators": [ { "creatorType": "author", "firstName": "Kilho", "lastName": "Shin" }, { "creatorType": "author", "firstName": "Xian Ming", "lastName": "Xu" }, { "creatorType": "editor", "firstName": "Juan D.", "lastName": "Velásquez" }, { "creatorType": "editor", "firstName": "Sebastián A.", "lastName": "Ríos" }, { "creatorType": "editor", "firstName": "Robert J.", "lastName": "Howlett" }, { "creatorType": "editor", "firstName": "Lakhmi C.", "lastName": "Jain" } ], "abstractNote": "Feature selection, the job to select features relevant to classification, is a central problem of machine learning. Inconsistency rate is known as an effective measure to evaluate consistency (relevance) of feature subsets, and INTERACT, a state-of-the-art feature selection algorithm, takes advantage of it. In this paper, we shows that inconsistency rate is not the unique measure of consistency by introducing two new consistency measures, and also, show that INTERACT has the important deficiency that it fails for particular types of probability distributions. To fix the deficiency, we propose two new algorithms, which have flexibility of taking advantage of any of the new measures as well as inconsistency rate. Furthermore, through experiments, we compare the three consistency measures, and prove effectiveness of the new algorithms.", "bookTitle": "Knowledge-Based and Intelligent Information and Engineering Systems", "series": "Lecture Notes in Computer Science", "seriesNumber": "5711", "volume": "", "numberOfVolumes": "", "edition": "", "date": "2009/01/01", "publisher": "Springer Berlin Heidelberg", "place": "", "originalDate": "", "originalPublisher": "", "originalPlace": "", "format": "", "pages": "342-350", "ISBN": "978-3-642-04594-3, 978-3-642-04595-0", "DOI": "", "citationKey": "", "url": "http://link.springer.com/chapter/10.1007/978-3-642-04595-0_42", "accessDate": "2014-01-15T09:56:53Z", "ISSN": "", "archive": "", "archiveLocation": "", "shortTitle": "", "language": "", "libraryCatalog": "link.springer.com", "callNumber": "", "rights": "©2009 Springer Berlin Heidelberg", "extra": "", "tags": [ { "tag": "Artificial Intelligence (incl. Robotics)", "type": 1 }, { "tag": "Computer Appl. in Administrative Data Processing", "type": 1 }, { "tag": "Computers and Society", "type": 1 }, { "tag": "Data Mining and Knowledge Discovery", "type": 1 }, { "tag": "Information Systems Applications (incl.Internet)", "type": 1 }, { "tag": "Management of Computing and Information Systems", "type": 1 } ], "collections": [], "relations": {}, "dateAdded": "2014-01-15T09:56:53Z", "dateModified": "2014-01-15T09:56:53Z" } }, { "key": "PKT6TEI2", "version": 674, "library": { "type": "group", "id": 23835, "name": "Feature Selection", "links": { "alternate": { "href": "https://www.zotero.org/groups/feature_selection", "type": "text/html" } } }, "links": { "self": { "href": "https://api.zotero.org/groups/23835/items/PKT6TEI2", "type": "application/json" }, "alternate": { "href": "https://www.zotero.org/groups/feature_selection/items/PKT6TEI2", "type": "text/html" }, "up": { "href": "https://api.zotero.org/groups/23835/items/HMIWAJV3", "type": "application/json" } }, "meta": { "createdByUser": { "id": 185063, "username": "arauzo", "name": "Antonio Arauzo-Azofra", "links": { "alternate": { "href": "https://www.zotero.org/arauzo", "type": "text/html" } } }, "numChildren": 0 }, "data": { "key": "PKT6TEI2", "version": 674, "parentItem": "HMIWAJV3", "itemType": "attachment", "linkMode": "imported_url", "title": "ScienceDirect Full Text PDF", "accessDate": "2013-06-25T11:09:06Z", "url": "http://www.sciencedirect.com/science?_ob=MiamiImageURL&_cid=271597&_user=723077&_pii=S0925231213002415&_check=y&_origin=article&_zone=toolbar&_coverDate=18-Jul-2013&view=c&originContentFamily=serial&wchp=dGLzVlV-zSkWb&md5=654b8d7c895c9e75b9556ddae611b834&pid=1-s2.0-S0925231213002415-main.pdf", "note": "", "contentType": "application/pdf", "charset": "", "filename": "Frénay et al. - 2013 - Theoretical and empirical study on the potential i.pdf", "md5": "06eb5f053cccbfeafd8be3b21c31f705", "mtime": 1372158552000, "tags": [], "relations": {}, "dateAdded": "2013-06-25T11:09:06Z", "dateModified": "2013-06-25T11:09:12Z" } }, { "key": "X2VJP3XQ", "version": 674, "library": { "type": "group", "id": 23835, "name": "Feature Selection", "links": { "alternate": { "href": "https://www.zotero.org/groups/feature_selection", "type": "text/html" } } }, "links": { "self": { "href": "https://api.zotero.org/groups/23835/items/X2VJP3XQ", "type": "application/json" }, "alternate": { "href": "https://www.zotero.org/groups/feature_selection/items/X2VJP3XQ", "type": "text/html" }, "up": { "href": "https://api.zotero.org/groups/23835/items/HMIWAJV3", "type": "application/json" } }, "meta": { "createdByUser": { "id": 185063, "username": "arauzo", "name": "Antonio Arauzo-Azofra", "links": { "alternate": { "href": "https://www.zotero.org/arauzo", "type": "text/html" } } } }, "data": { "key": "X2VJP3XQ", "version": 674, "parentItem": "HMIWAJV3", "itemType": "attachment", "linkMode": "imported_url", "title": "ScienceDirect Snapshot", "accessDate": "2013-06-25T11:09:06Z", "url": "http://www.sciencedirect.com/science/article/pii/S0925231213002415", "note": "", "contentType": "text/html", "charset": "utf-8", "filename": "S0925231213002415.html", "md5": "5d2a8dfe1c3bccdd184fd188d11c40ac", "mtime": 1372158548000, "tags": [], "relations": {}, "dateAdded": "2013-06-25T11:09:06Z", "dateModified": "2013-06-25T11:09:06Z" } }, { "key": "HMIWAJV3", "version": 673, "library": { "type": "group", "id": 23835, "name": "Feature Selection", "links": { "alternate": { "href": "https://www.zotero.org/groups/feature_selection", "type": "text/html" } } }, "links": { "self": { "href": "https://api.zotero.org/groups/23835/items/HMIWAJV3", "type": "application/json" }, "alternate": { "href": "https://www.zotero.org/groups/feature_selection/items/HMIWAJV3", "type": "text/html" } }, "meta": { "createdByUser": { "id": 185063, "username": "arauzo", "name": "Antonio Arauzo-Azofra", "links": { "alternate": { "href": "https://www.zotero.org/arauzo", "type": "text/html" } } }, "creatorSummary": "Frénay et al.", "parsedDate": "2013-07-18", "numChildren": 2 }, "data": { "key": "HMIWAJV3", "version": 673, "itemType": "journalArticle", "title": "Theoretical and empirical study on the potential inadequacy of mutual information for feature selection in classification", "creators": [ { "creatorType": "author", "firstName": "Benoît", "lastName": "Frénay" }, { "creatorType": "author", "firstName": "Gauthier", "lastName": "Doquire" }, { "creatorType": "author", "firstName": "Michel", "lastName": "Verleysen" } ], "abstractNote": "Mutual information is a widely used performance criterion for filter feature selection. However, despite its popularity and its appealing properties, mutual information is not always the most appropriate criterion. Indeed, contrary to what is sometimes hypothesized in the literature, looking for a feature subset maximizing the mutual information does not always guarantee to decrease the misclassification probability, which is often the objective one is interested in. The first objective of this paper is thus to clearly illustrate this potential inadequacy and to emphasize the fact that the mutual information remains a heuristic, coming with no guarantee in terms of classification accuracy. Through extensive experiments, a deeper analysis of the cases for which the mutual information is not a suitable criterion is then conducted. This analysis allows us to confirm the general interest of the mutual information for feature selection. It also helps us better apprehending the behaviour of mutual information throughout a feature selection process and consequently making a better use of it as a feature selection criterion.", "publicationTitle": "Neurocomputing", "publisher": "", "place": "", "date": "July 18, 2013", "volume": "112", "issue": "", "section": "", "partNumber": "", "partTitle": "", "pages": "64-78", "series": "", "seriesTitle": "", "seriesText": "", "journalAbbreviation": "Neurocomputing", "DOI": "10.1016/j.neucom.2012.12.051", "citationKey": "", "url": "http://www.sciencedirect.com/science/article/pii/S0925231213002415", "accessDate": "2013-06-25T11:09:06Z", "PMID": "", "PMCID": "", "ISSN": "0925-2312", "archive": "", "archiveLocation": "", "shortTitle": "Advances in artificial neural networks, machine learning, and computational intelligence Selected papers from the 20th European Symposium on Artificial Neural Networks (ESANN 2012)", "language": "", "libraryCatalog": "ScienceDirect", "callNumber": "", "rights": "", "extra": "", "tags": [ { "tag": "Feature selection", "type": 1 }, { "tag": "Hellman–Raviv and Fano bounds", "type": 1 }, { "tag": "Mutual information", "type": 1 }, { "tag": "Probability of misclassification", "type": 1 }, { "tag": "classification", "type": 1 } ], "collections": [], "relations": {}, "dateAdded": "2013-06-25T11:09:06Z", "dateModified": "2013-06-25T11:09:06Z" } }, { "key": "7NFQS2VH", "version": 2, "library": { "type": "group", "id": 23835, "name": "Feature Selection", "links": { "alternate": { "href": "https://www.zotero.org/groups/feature_selection", "type": "text/html" } } }, "links": { "self": { "href": "https://api.zotero.org/groups/23835/items/7NFQS2VH", "type": "application/json" }, "alternate": { "href": "https://www.zotero.org/groups/feature_selection/items/7NFQS2VH", "type": "text/html" }, "up": { "href": "https://api.zotero.org/groups/23835/items/SHQ49ZH3", "type": "application/json" } }, "meta": { "createdByUser": { "id": 185063, "username": "arauzo", "name": "Antonio Arauzo-Azofra", "links": { "alternate": { "href": "https://www.zotero.org/arauzo", "type": "text/html" } } }, "numChildren": 0 }, "data": { "key": "7NFQS2VH", "version": 2, "parentItem": "SHQ49ZH3", "itemType": "attachment", "linkMode": "imported_file", "title": "Bellal2012a_semi-supervised_feature_ranking.pdf", "accessDate": "", "url": "", "note": "", "contentType": "application/pdf", "charset": "", "filename": "Bellal2012a_semi-supervised_feature_ranking.pdf", "md5": "a4bbe6e46b1ea15d07ad2ebb0d507801", "mtime": 1338197651000, "tags": [], "relations": {}, "dateAdded": "2012-05-28T09:34:11Z", "dateModified": "2012-05-28T09:34:11Z" } }, { "key": "SHQ49ZH3", "version": 1, "library": { "type": "group", "id": 23835, "name": "Feature Selection", "links": { "alternate": { "href": "https://www.zotero.org/groups/feature_selection", "type": "text/html" } } }, "links": { "self": { "href": "https://api.zotero.org/groups/23835/items/SHQ49ZH3", "type": "application/json" }, "alternate": { "href": "https://www.zotero.org/groups/feature_selection/items/SHQ49ZH3", "type": "text/html" } }, "meta": { "createdByUser": { "id": 185063, "username": "arauzo", "name": "Antonio Arauzo-Azofra", "links": { "alternate": { "href": "https://www.zotero.org/arauzo", "type": "text/html" } } }, "creatorSummary": "Bellal et al.", "parsedDate": "2012-07-15", "numChildren": 3 }, "data": { "key": "SHQ49ZH3", "version": 1, "itemType": "journalArticle", "title": "A semi-supervised feature ranking method with ensemble learning", "creators": [ { "creatorType": "author", "firstName": "Fazia", "lastName": "Bellal" }, { "creatorType": "author", "firstName": "Haytham", "lastName": "Elghazel" }, { "creatorType": "author", "firstName": "Alex", "lastName": "Aussem" } ], "abstractNote": "We consider the problem of using a large amount of unlabeled data to improve the efficiency of feature selection in high-dimension when only a small amount of labeled examples is available. We propose a new method called semi-supervised ensemble learning guided feature ranking method (SEFR for short), that combines a bagged ensemble of standard semi-supervised approaches with a permutation-based out-of-bag feature importance measure that takes into account both labeled and unlabeled data. 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