{"type":"ScholarlyArticle","title":"Learning Homophily Couplings from Non-IID Data for Joint Feature Selection and Noise-Resilient Outlier Detection","authors":[{"name":"Guansong Pang","slug":"guansong-pang","given":"Guansong","family":"Pang","affiliation":"† School of Software , University of Technology Sydney , Australia"},{"name":"Longbing Cao","slug":"longbing-cao","given":"Longbing","family":"Cao","affiliation":"† School of Software , University of Technology Sydney , Australia"},{"name":"Ling Chen","slug":"ling-chen","given":"Ling","family":"Chen","affiliation":"† School of Software , University of Technology Sydney , Australia"},{"name":"Huan Liu","slug":"huan-liu","given":"Huan","family":"Liu","affiliation":"‡ Computer Science and Engineering , Arizona State University , USA"}],"abstract":"This paper introduces a novel wrapper-based outlier detection framework (WrapperOD) and its instance (HOUR) for identifying outliers in noisy data (i.e., data with noisy features) with strong couplings between outlying behaviors. Existing subspace or feature selection-based methods are significantly challenged by such data, as their search of feature subset(s) is independent of outlier scoring and thus can be misled by noisy features. In contrast, HOUR takes a wrapper approach to iteratively optimize the feature subset selection and outlier scoring using a top-k outlier ranking evaluation measure as its objective function. HOUR learns homophily couplings between outlying behaviors (i.e., abnormal behaviors are not independent - they bond together) in constructing a noise-resilient outlier scoring function to produce a reliable outlier ranking in each iteration. We show that HOUR (i) retains a 2-approximation outlier ranking to the optimal one; and (ii) significantly outperforms five state-of-the-art competitors on 15 real-world data sets with different noise levels in terms of AUC and/or P@n. The source code of HOUR is available at https://sites.google.com/site/gspangsite/sourcecode.","doi":"10.24963/ijcai.2017/360","url":"https://ijcai.org/proceedings/2017/360","pdf":"https://ijcai.org/proceedings/2017/0360.pdf","markdown":"https://ijcai.org/proceedings/2017/360.md","bibtex":"https://ijcai.org/proceedings/2017/bibtex/360","volume":{"slug":"2017","title":"Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2017","url":"https://ijcai.org/proceedings/2017/","year":2017,"location":"Melbourne, Australia","dates":"19-25 August 2017","editors":"Carles Sierra","isbn":"978-0-9992411-0-3","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2017"},"section":"Machine Learning A-R","track":"Main track","number":360,"pages":{"first":2585,"last":2591},"keywords":["Machine Learning: Data Mining","Machine Learning: Feature Selection/Construction"],"videos":[],"references":[{"position":1,"raw":"[Akoglu et al., 2012] Leman Akoglu, Hanghang Tong, Jilles Vreeken, and Christos Faloutsos. Fast and reliable anomaly detection in categorical data. In CIKM, pages 415-424. ACM, 2012.","title":"Fast and reliable anomaly detection in categorical data","authors":"Leman Akoglu, Hanghang Tong, Jilles Vreeken, Christos Faloutsos","year":"2012","venue":"CIKM","doi":null,"url":null,"internal_url":null},{"position":2,"raw":"[Angiulli et al., 2009] Fabrizio Angiulli, Fabio Fassetti, and Luigi Palopoli. Detecting outlying properties of excep- tional objects. ACM Transactions on Database Systems, 34(1):7, 2009.","title":"Detecting outlying properties of exceptional objects","authors":"Fabrizio Angiulli, Fabio Fassetti, Luigi Palopoli","year":"2009","venue":"ACM Transactions on Database Systems","doi":null,"url":null,"internal_url":null},{"position":3,"raw":"[Azmandian et al., 2012] Fatemeh Azmandian, Ayse Yil- mazer, Jennifer G Dy, Javed Aslam, David R Kaeli, et al. GPU-accelerated feature selection for outlier detection us- ing the local kernel density ratio. In ICDM, pages 51-60. IEEE, 2012.","title":"GPU-accelerated feature selection for outlier detection using the local kernel density ratio","authors":"Ayse Fatemeh Azmandian, Jennifer G Yilmazer, Javed Dy, Aslam, David R Kaeli","year":"2012","venue":"ICDM","doi":null,"url":null,"internal_url":null},{"position":4,"raw":"[Cao et al., 2012] Longbing Cao, Yuming Ou, and Philip S Yu. Coupled behavior analysis with applications. IEEE Transactions on Knowledge and Data Engineering, 24(8):1378-1392, 2012.","title":"Coupled behavior analysis with applications","authors":"Cao","year":"2012","venue":"IEEE Transactions on Knowledge and Data Engineering","doi":null,"url":null,"internal_url":null},{"position":5,"raw":"[Cao, 2014] Longbing Cao. Non-iidness learning in behav- ioral and social data. The Computer Journal, 57(9):1358- 1370, 2014.","title":"Longbing Cao. Non-iidness learning in behavioral and social data","authors":"Cao","year":"2014","venue":"The Computer Journal","doi":null,"url":null,"internal_url":null},{"position":6,"raw":"[Cao, 2015] Longbing Cao. Coupling learning of complex interactions. Information Processing & Management, 51(2):167-186, 2015.","title":"Longbing Cao. Coupling learning of complex interactions","authors":"Cao","year":"2015","venue":"Information Processing & Management","doi":null,"url":null,"internal_url":null},{"position":7,"raw":"[Chandola et al., 2009] Varun Chandola, Arindam Banerjee, and Vipin Kumar. Anomaly detection: A survey. ACM Computing Surveys, 41(3):15, 2009.","title":"Anomaly detection: A survey","authors":"Varun Chandola, Arindam Banerjee, Vipin Kumar","year":"2009","venue":"ACM Computing Surveys","doi":null,"url":null,"internal_url":null},{"position":8,"raw":"[Chau et al., 2011] Duen Horng Polo Chau, Carey Nachen- berg, Jeffrey Wilhelm, Adam Wright, and Christos Falout- sos. Polonium: Tera-scale graph mining and inference for malware detection. In SDM, pages 131-142. SIAM, 2011.","title":"Polonium: Tera-scale graph mining and inference for malware detection","authors":"Duen Horng, Polo Chau, Carey Nachenberg, Jeffrey Wilhelm, Adam Wright, Christos Faloutsos","year":"2011","venue":"SDM","doi":null,"url":null,"internal_url":null},{"position":9,"raw":"[Chen et al., 2016] Ting Chen, Lu-An Tang, Yizhou Sun, Zhengzhang Chen, and Kai Zhang. Entity embedding- based anomaly detection for heterogeneous categorical events. In IJCAI, pages 1396-1403. AAAI, 2016.","title":"Entity embeddingbased anomaly detection for heterogeneous categorical events","authors":"Ting Chen, Lu-An Tang, Yizhou Sun, Zhengzhang Chen, Kai Zhang","year":"2016","venue":"IJCAI","doi":null,"url":null,"internal_url":null},{"position":10,"raw":"[Cinbis et al., 2016] Ramazan Gokberk Cinbis, Jakob Ver- beek, and Cordelia Schmid. Approximate fisher kernels of non-iid image models for image categorization. IEEE Transactions on Pattern Analysis and Machine Intelli- gence, 38(6):1084-1098, 2016.","title":"Ramazan Gokberk Cinbis, Jakob Verbeek, and Cordelia Schmid. Approximate fisher kernels of non-iid image models for image categorization","authors":"Cinbis","year":"2016","venue":"IEEE Transactions on Pattern Analysis and Machine Intelligence","doi":null,"url":null,"internal_url":null},{"position":11,"raw":"[Hall et al., 2009] Mark Hall, Eibe Frank, Geoffrey Holmes, Bernhard Pfahringer, Peter Reutemann, and Ian H Wit- ten. The WEKA data mining software: An update. ACM SIGKDD Explorations Newsletter, 11(1):10-18, 2009.","title":"The WEKA data mining software: An update","authors":"Mark Hall, Eibe Frank, Geoffrey Holmes, Bernhard Pfahringer, Peter Reutemann, Ian H Witten","year":"2009","venue":"ACM SIGKDD Explorations Newsletter","doi":null,"url":null,"internal_url":null},{"position":12,"raw":"[He and Carbonell, 2010] Jingrui He and Jaime Carbonell. Coselection of features and instances for unsupervised rare category analysis. Statistical Analysis and Data Mining, 3(6):417-430, 2010.","title":"Jingrui He and Jaime Carbonell. Coselection of features and instances for unsupervised rare category analysis","authors":"Carbonell He","year":"2010","venue":"Statistical Analysis and Data Mining","doi":null,"url":null,"internal_url":null},{"position":13,"raw":"[He et al., 2005] Zengyou He, Xiaofei Xu, Zhexue Joshua Huang, and Shengchun Deng. FP-outlier: Frequent pat- tern based outlier detection. Computer Science and Infor- mation Systems, 2(1):103-118, 2005.","title":"FP-outlier: Frequent pattern based outlier detection","authors":"He","year":"2005","venue":"Computer Science and Information Systems","doi":null,"url":null,"internal_url":null},{"position":14,"raw":"[Ho and Basu, 2002] Tin Kam Ho and Mitra Basu. Com- plexity measures of supervised classification problems. IEEE Transactions on Pattern Analysis and Machine In- telligence, 24(3):289-300, 2002.","title":"Complexity measures of supervised classification problems","authors":"Basu ; Tin Ho, Kam Ho, Mitra Basu","year":"2002","venue":"IEEE Transactions on Pattern Analysis and Machine Intelligence","doi":null,"url":null,"internal_url":null},{"position":15,"raw":"[Jeong et al., 2012] Young-Seon Jeong, In-Ho Kang, Myong-Kee Jeong, and Dongjoon Kong. A new feature selection method for one-class classification problems. IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews), 42(6):1500-1509, 2012.","title":"A new feature selection method for one-class classification problems","authors":"Jeong","year":"2012","venue":"IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews)","doi":null,"url":null,"internal_url":null},{"position":16,"raw":"[Keller et al., 2012] Fabian Keller, Emmanuel Muller, and Klemens Bohm. HiCS: High contrast subspaces for density-based outlier ranking. In ICDE, pages 1037-1048. IEEE, 2012.","title":"HiCS: High contrast subspaces for density-based outlier ranking","authors":"Fabian Keller, Emmanuel Muller, Klemens Bohm","year":"2012","venue":"ICDE","doi":null,"url":null,"internal_url":null},{"position":17,"raw":"[Kohavi and John, 1997] Ron Kohavi and George H John. Wrappers for feature subset selection. Artificial intelli- gence, 97(1):273-324, 1997.","title":"Wrappers for feature subset selection","authors":"John ; Kohavi, Ron Kohavi, George H John","year":"1997","venue":"Artificial intelligence","doi":null,"url":null,"internal_url":null},{"position":18,"raw":"[Lazarevic and Kumar, 2005] Aleksandar Lazarevic and Vipin Kumar. Feature bagging for outlier detection. In SIGKDD, pages 157-166. ACM, 2005.","title":"Feature bagging for outlier detection","authors":"Kumar Lazarevic, Aleksandar Lazarevic, Vipin Kumar","year":"2005","venue":"SIGKDD","doi":null,"url":null,"internal_url":null},{"position":19,"raw":"[Leyva et al., 2015] Enrique Leyva, Antonio González, and Raul Perez. A set of complexity measures designed for applying meta-learning to instance selection. IEEE Trans- actions on Knowledge and Data Engineering, 27(2):354- 367, 2015.","title":"A set of complexity measures designed for applying meta-learning to instance selection","authors":"Enrique Leyva, Antonio González, Raul Perez","year":"2015","venue":"IEEE Transactions on Knowledge and Data Engineering","doi":null,"url":null,"internal_url":null},{"position":20,"raw":"[Li et al., 2016] Jundong Li, Kewei Cheng, Suhang Wang, Fred Morstatter, Robert P Trevino, Jiliang Tang, and Huan Liu. Feature selection: A data perspective. CoRR, abs/1601.07996, 2016.","title":"Feature selection: A data perspective","authors":"Jundong Li, Kewei Cheng, Suhang Wang, Fred Morstatter, Robert P Trevino, Jiliang Tang, Huan Liu","year":"2016","venue":null,"doi":null,"url":null,"internal_url":null},{"position":21,"raw":"[Lorena et al., 2015] Luiz HN Lorena, André CPLF Car- valho, and Ana C Lorena. Filter feature selection for one- class classification. Journal of Intelligent & Robotic Sys- tems, 80(1):227-243, 2015.","title":"Filter feature selection for oneclass classification","authors":"André Cplf Luiz Hn Lorena, Ana C Carvalho, Lorena","year":"2015","venue":"Journal of Intelligent & Robotic Systems","doi":null,"url":null,"internal_url":null},{"position":22,"raw":"[Marques et al., 2015] Henrique O Marques, Ricardo JGB Campello, Arthur Zimek, and Jörg Sander. On the internal evaluation of unsupervised outlier detection. In SSDBM. ACM, 2015.","title":"On the internal evaluation of unsupervised outlier detection","authors":"Ricardo Jgb Henrique O Marques, Arthur Campello, Jörg Zimek, Sander","year":"2015","venue":"SSDBM. ACM","doi":null,"url":null,"internal_url":null},{"position":23,"raw":"[Pang et al., 2016a] Guansong Pang, Longbing Cao, and Ling Chen. Outlier detection in complex categorical data by modelling the feature value couplings. In IJCAI, pages 1902-1908. AAAI, 2016.","title":"Outlier detection in complex categorical data by modelling the feature value couplings","authors":"Pang","year":"2016","venue":"IJCAI","doi":null,"url":null,"internal_url":null},{"position":24,"raw":"[Pang et al., 2016b] Guansong Pang, Longbing Cao, Ling Chen, and Huan Liu. Unsupervised feature selection for outlier detection by modelling hierarchical value-feature couplings. In ICDM. IEEE, 2016.","title":"Unsupervised feature selection for outlier detection by modelling hierarchical value-feature couplings","authors":"Pang","year":"2016","venue":"ICDM. IEEE","doi":null,"url":null,"internal_url":null},{"position":25,"raw":"[Pang et al., 2016c] Guansong Pang, Kai Ming Ting, David Albrecht, and Huidong Jin. Zero++: Harnessing the power of zero appearances to detect anomalies in large-scale data sets. Journal of Artificial Intelligence Research, 57:593- 620, 2016.","title":"Zero++: Harnessing the power of zero appearances to detect anomalies in large-scale data sets","authors":"Pang","year":"2016","venue":"Journal of Artificial Intelligence Research","doi":null,"url":null,"internal_url":null},{"position":26,"raw":"[Zimek et al., 2012] Arthur Zimek, Erich Schubert, and Hans-Peter Kriegel. A survey on unsupervised outlier detection in high-dimensional numerical data. Statistical Analysis and Data Mining, 5(5):363-387, 2012.","title":"A survey on unsupervised outlier detection in high-dimensional numerical data","authors":"Zimek","year":"2012","venue":"Statistical Analysis and Data Mining","doi":null,"url":null,"internal_url":null}],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}