research-article

Mining emerging patterns by streaming feature selection

Authors:

Dan A. Simovici,

Xindong WuAuthors Info & Claims

KDD '12: Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining

Pages 60 - 68

https://doi.org/10.1145/2339530.2339544

Published: 12 August 2012 Publication History

Abstract

Building an accurate emerging pattern classifier with a high-dimensional dataset is a challenging issue. The problem becomes even more difficult if the whole feature space is unavailable before learning starts. This paper presents a new technique on mining emerging patterns using streaming feature selection. We model high feature dimensions with streaming features, that is, features arrive and are processed one at a time. As features flow in one by one, we online evaluate each coming feature to determine whether it is useful for mining predictive emerging patterns (EPs) by exploiting the relationship between feature relevance and EP discriminability (the predictive ability of an EP). We employ this relationship to guide an online EP mining process. This new approach can mine EPs from a high-dimensional dataset, even when its entire feature set is unavailable before learning. The experiments on a broad range of datasets validate the effectiveness of the proposed approach against other well-established methods, in terms of predictive accuracy, pattern numbers and running time.

References

[1]

C. F. Aliferis, I. Tsamardinos, A. Statnikov & L.E. Brown. (2003) Causal Explorer: a causal probabilistic network learning toolkit for biomedical discovery. METMBS'03.

[2]

Roberto J. Bayardo. (1998) Efficiently mining long patterns from databases. SIGMOD'98, 85--93.

Digital Library

[3]

J. Bailey, T. Manoukian & K. Ramamohanarao. (2002) Fast algorithms for mining emerging patterns. PKDD'02, 39--50.

Digital Library

[4]

J. Bailey, T. Manoukian & K. Ramamohanarao. (2003) A fast algorithm for computing hypergraph transversals and its application in mining emerging patterns. ICDM'03, 485--488.

Digital Library

[5]

C. L. Blake & C. J. Merz. (1998) UCI Repository of Machine Learning Databases.

[6]

H. Fan & K. Ramamohanarao. (2002) An efficient single-scan algorithm for mining essential jumping emerging patterns for classification. PAKDD'02, 456--462.

Digital Library

[7]

H. Fan & K. Ramamohanarao. (2006) Fast discovery and the generalization of strong jumping emerging patterns for building compact and accurate classifiers. IEEE Transactions on Knowledge and Data Engineering, 18(6), 721--737.

Digital Library

[8]

G. Fang, G. Pandey, W. Wang, M. Gupta, M. Steinbach, & V. Kumar. (2012) Mining low-support discriminative patterns from dense and high-dimensional data. IEEE Transactions on Knowledge and Data Engineering, 24(2), 279--294.

Digital Library

[9]

G. Dong & J. Li. (1999) Efficient mining of emerging patterns: discovering trends and differences. KDD'99, 43--52.

Digital Library

[10]

G. Dong, X. Zhang, L. Wong, & J. Li. (1999) CAEP: Classification by Aggregating Emerging Patterns. DS'99, 30--42.

Digital Library

[11]

R. Kohavi & G. H. John. (1997) Wrappers for feature subset selection. Artificial Intelligence, 97, 273--324.

Digital Library

[12]

J. Li, G. Dong, & K. Ramamohanarao. (2000) Making use of the most expressive jumping emerging patterns for classification. PAKDD'00, 220--232.

Digital Library

[13]

J. Li, G. Dong & K. Ramamohanarao (2000). Instance-based classification by emerging patterns. PKDD'00, 191--200.

Digital Library

[14]

W. Li, J. Han, & J. Pei. (2001) CMAR: accurate and efficient classification based on multiple-class association rule. ICDM'01, 369--376.

Digital Library

[15]

B. Liu, W. Hsu, & Y. Ma. (1998) Integrating classification and association rule mining. KDD'98, 80--86.

[16]

D. Lo, H. Cheng, J. Han, S. Khoo, & C. Sun. (2009) Classification of software behaviors for failure detection: a discriminative pattern mining approach. KDD'09, 557--566.

Digital Library

[17]

E. Loekito & J. Bailey. (2006) Fast mining of high dimensional expressive contrast patterns using zero suppressed binary decision diagrams. KDD'06, 307--316.

Digital Library

[18]

S. Mao & G. Dong. (2005) Discovery of highly differentiative gene groups from microarray gene expression data using the gene club approach. J. Bioinformatics and Computational Biology, 3(6):1263--1280.

[19]

X. Wu, K.Yu, H. Wang & W. Ding. (2010) Online streaming feature selection. ICML'10, 1159--1166.

[20]

X. Yin & J. Han. (2003) CPAR: classification based on predictive association rule. SDM'03, 369--376.

[21]

K. Yu, X. Wu, W. Ding, H. Wang & H. Yao. (2011) Causal associative classification. ICDM'11, 914--923.

Digital Library

[22]

L. Yu & H. Liu. (2004) Efficient feature selection via analysis of relevance and redundancy. J. of Machine Learning Research, 5, 1205--1224.

Digital Library

[23]

X. Zhang, G. Dong & K. Ramamohanarao. (2000) Exploring constraints to efficiently mine emerging patterns from large high-dimensional datasets. KDD'00, 310--314.

Digital Library

[24]

J. Zhou, D. Foster, R. A. Stine & L. H. Ungar. (2006) Streamwise feature selection. J. of Machine Learning Research, 7, 1861--1885.

Digital Library

Cited By

Satyanarayana GChatrapathi K(2023)Survey on Imbalanced Dataset Classification—Machine LearningIntelligent Systems and Sustainable Computing10.1007/978-981-99-4717-1_19(207-216)Online publication date: 3-Oct-2023
https://doi.org/10.1007/978-981-99-4717-1_19
Motl JKordík P(2022)Discriminant Analysis on a Stream of FeaturesEngineering Applications of Neural Networks10.1007/978-3-031-08223-8_19(223-234)Online publication date: 10-Jun-2022
https://doi.org/10.1007/978-3-031-08223-8_19
Loyola-González OMedina-Pérez MChoo K(2020)A Review of Supervised Classification based on Contrast Patterns: Applications, Trends, and ChallengesJournal of Grid Computing10.1007/s10723-020-09526-yOnline publication date: 4-Oct-2020
https://doi.org/10.1007/s10723-020-09526-y
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Index Terms

Mining emerging patterns by streaming feature selection
1. Computing methodologies
  1. Machine learning

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Published In

cover image ACM Conferences

KDD '12: Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining

August 2012

1616 pages

ISBN:9781450314626

DOI:10.1145/2339530

General Chair:
Qiang Yang
Hong Kong University of Science and Technology
,
Program Chairs:
Deepak Agarwal
LinkedIn
,
Jian Pei
Simon Fraser University

Copyright © 2012 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Association for Computing Machinery

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Publication History

Published: 12 August 2012

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KDD '12

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KDD '12: The 18th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining

August 12 - 16, 2012

Beijing, China

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Overall Acceptance Rate 1,133 of 8,635 submissions, 13%

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Cited By

Satyanarayana GChatrapathi K(2023)Survey on Imbalanced Dataset Classification—Machine LearningIntelligent Systems and Sustainable Computing10.1007/978-981-99-4717-1_19(207-216)Online publication date: 3-Oct-2023
https://doi.org/10.1007/978-981-99-4717-1_19
Motl JKordík P(2022)Discriminant Analysis on a Stream of FeaturesEngineering Applications of Neural Networks10.1007/978-3-031-08223-8_19(223-234)Online publication date: 10-Jun-2022
https://doi.org/10.1007/978-3-031-08223-8_19
Loyola-González OMedina-Pérez MChoo K(2020)A Review of Supervised Classification based on Contrast Patterns: Applications, Trends, and ChallengesJournal of Grid Computing10.1007/s10723-020-09526-yOnline publication date: 4-Oct-2020
https://doi.org/10.1007/s10723-020-09526-y
Octavio Loyola-Gonzalez OMartinez-Trinidad JCarrasco-Ochoa JGarcia-Borroto M(2019)Cost-Sensitive Pattern-Based classification for Class Imbalance problemsIEEE Access10.1109/ACCESS.2019.29139827(60411-60427)Online publication date: 2019
https://doi.org/10.1109/ACCESS.2019.2913982
Ali ZShahzad W(2017)EPACO: a novel ant colony optimization for emerging patterns based classificationCluster Computing10.1007/s10586-017-0894-421:1(453-467)Online publication date: 18-May-2017
https://doi.org/10.1007/s10586-017-0894-4
Du HSu YLi C(2016)Discriminative Sequential Pattern Mining for Software Failure DetectionProceedings of the 10th International Conference on Informatics and Systems10.1145/2908446.2908453(153-158)Online publication date: 9-May-2016
https://dl.acm.org/doi/10.1145/2908446.2908453
Loyola-González OMartínez-Trinidad JCarrasco-Ochoa JGarcía-Borroto M(2016)Study of the impact of resampling methods for contrast pattern based classifiers in imbalanced databasesNeurocomputing10.1016/j.neucom.2015.04.120175:PB(935-947)Online publication date: 29-Jan-2016
https://dl.acm.org/doi/10.1016/j.neucom.2015.04.120
Yu KDing WSimovici DWang HPei JWu X(2015)Classification with Streaming Features: An Emerging-Pattern Mining ApproachACM Transactions on Knowledge Discovery from Data10.1145/27004099:4(1-31)Online publication date: 1-Jun-2015
https://dl.acm.org/doi/10.1145/2700409
Alavi FHashemi S(2015)DFP-SEPSF: A dynamic frequent pattern tree to mine strong emerging patterns in streamwise featuresEngineering Applications of Artificial Intelligence10.1016/j.engappai.2014.08.01037(54-70)Online publication date: Jan-2015
https://doi.org/10.1016/j.engappai.2014.08.010
Hamed AWu XRubin A(2014)A twitter recruitment intelligent system: association rule mining for smoking cessationSocial Network Analysis and Mining10.1007/s13278-014-0212-64:1Online publication date: 12-Aug-2014
https://doi.org/10.1007/s13278-014-0212-6
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