Mining Jumping Emerging Patterns by Streaming Feature Selection

One of the main challenges for making an accurate classifier based on mining emerging pattern is extraction of a minimal set of strong emerging patterns from a high-dimensional dataset. This problem is harder when features are generated dynamically and so the entire feature space is unavailable. In this scheme, features are obtained one by one instead of having all features available before learning starts.

[1]  Corinna Cortes,et al.  Support-Vector Networks , 1995, Machine Learning.

[2]  Xindong Wu,et al.  Mining emerging patterns by streaming feature selection , 2012, KDD.

[3]  Jinyan Li,et al.  CAEP: Classification by Aggregating Emerging Patterns , 1999, Discovery Science.

[4]  Kotagiri Ramamohanarao,et al.  The Space of Jumping Emerging Patterns and Its Incremental Maintenance Algorithms , 2000, ICML.

[5]  Hao Wang,et al.  Online Streaming Feature Selection , 2010, ICML.

[6]  Gerhard Tutz,et al.  A CART-based approach to discover emerging patterns in microarray data , 2003, Bioinform..

[7]  Roberto J. Bayardo,et al.  Efficiently mining long patterns from databases , 1998, SIGMOD '98.

[8]  Manish Gupta,et al.  Mining Low-Support Discriminative Patterns from Dense and High-Dimensional Data , 2012, IEEE Transactions on Knowledge and Data Engineering.

[9]  Huiqing Liu,et al.  Simple rules underlying gene expression profiles of more than six subtypes of acute lymphoblastic leukemia (ALL) patients , 2003, Bioinform..

[10]  Jiawei Han,et al.  Classification of software behaviors for failure detection: a discriminative pattern mining approach , 2009, KDD.

[11]  Yoav Freund,et al.  A decision-theoretic generalization of on-line learning and an application to boosting , 1997, EuroCOLT.

[12]  Kotagiri Ramamohanarao,et al.  Making Use of the Most Expressive Jumping Emerging Patterns for Classification , 2000, Knowledge and Information Systems.

[13]  Jian Pei,et al.  Mining frequent patterns without candidate generation , 2000, SIGMOD 2000.

[14]  Guozhu Dong,et al.  Discovery of Highly Differentiative Gene Groups from Microarray Gene Expression Data Using the Gene Club Approach , 2005, J. Bioinform. Comput. Biol..

[15]  Jinyan Li,et al.  Efficient mining of emerging patterns: discovering trends and differences , 1999, KDD '99.

[16]  Kotagiri Ramamohanarao,et al.  An Efficient Single-Scan Algorithm for Mining Essential Jumping Emerging Patterns for Classification , 2002, PAKDD.

[17]  Jinyan Li,et al.  Mining border descriptions of emerging patterns from dataset pairs , 2005, Knowledge and Information Systems.

[18]  J. Ross Quinlan,et al.  C4.5: Programs for Machine Learning , 1992 .

[19]  James Bailey,et al.  Fast mining of high dimensional expressive contrast patterns using zero-suppressed binary decision diagrams , 2006, KDD '06.

[20]  Jing Zhou,et al.  Streamwise Feature Selection , 2006, J. Mach. Learn. Res..

[21]  Jinyan Li,et al.  Identifying good diagnostic gene groups from gene expression profiles using the concept of emerging patterns. , 2002 .