TY - GEN
T1 - NDPMine
T2 - European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, ECML PKDD 2010
AU - Kim, Hyungsul
AU - Kim, Sangkyum
AU - Weninger, Tim
AU - Han, Jiawei
AU - Abdelzaher, Tarek
N1 - Funding Information:
Research was sponsored in part by the U.S. National Science Foundation under grants CCF-0905014, and CNS-0931975, Air Force Office of Scientific Research MURI award FA9550-08-1-0265, and by the Army Research Laboratory under Cooperative Agreement Number W911NF-09-2-0053 (NS-CTA). The views and conclusions contained in this document are those of the authors and should not be interpreted as representing the official policies, either expressed or implied, of the Army Research Laboratory or the U.S. Government. The U.S. Government is authorized to reproduce and distribute reprints for Government purposes notwithstanding any copyright notation here on. The second author was supported by the National Science Foundation OCI-07-25070 and the state of Illinois. The third author was supported by a NDSEG PhD Fellowship.
PY - 2010
Y1 - 2010
N2 - Pattern-based classification has demonstrated its power in recent studies, but because the cost of mining discriminative patterns as features in classification is very expensive, several efficient algorithms have been proposed to rectify this problem. These algorithms assume that feature values of the mined patterns are binary, i.e., a pattern either exists or not. In some problems, however, the number of times a pattern appears is more informative than whether a pattern appears or not. To resolve these deficiencies, we propose a mathematical programming method that directly mines discriminative patterns as numerical features for classification. We also propose a novel search space shrinking technique which addresses the inefficiencies in iterative pattern mining algorithms. Finally, we show that our method is an order of magnitude faster, significantly more memory efficient and more accurate than current approaches.
AB - Pattern-based classification has demonstrated its power in recent studies, but because the cost of mining discriminative patterns as features in classification is very expensive, several efficient algorithms have been proposed to rectify this problem. These algorithms assume that feature values of the mined patterns are binary, i.e., a pattern either exists or not. In some problems, however, the number of times a pattern appears is more informative than whether a pattern appears or not. To resolve these deficiencies, we propose a mathematical programming method that directly mines discriminative patterns as numerical features for classification. We also propose a novel search space shrinking technique which addresses the inefficiencies in iterative pattern mining algorithms. Finally, we show that our method is an order of magnitude faster, significantly more memory efficient and more accurate than current approaches.
KW - Discriminative Pattern Mining
KW - Pattern-Based Classification
KW - SVM
UR - http://www.scopus.com/inward/record.url?scp=78049390686&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=78049390686&partnerID=8YFLogxK
U2 - 10.1007/978-3-642-15883-4_3
DO - 10.1007/978-3-642-15883-4_3
M3 - Conference contribution
AN - SCOPUS:78049390686
SN - 364215882X
SN - 9783642158827
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 35
EP - 50
BT - Machine Learning and Knowledge Discovery in Databases - European Conference, ECML PKDD 2010, Proceedings
Y2 - 20 September 2010 through 24 September 2010
ER -