Evolving linear discriminant in a continuously growing dimensional space for incremental attribute learning

Ting Wang; Sheng Uei Guan; T. O. Ting; Ka Lok Man; Fei Liu

doi:10.1007/978-3-642-35606-3_57

Evolving linear discriminant in a continuously growing dimensional space for incremental attribute learning

Ting Wang^*, Sheng Uei Guan, T. O. Ting, Ka Lok Man, Fei Liu

^*Corresponding author for this work

Research output: Chapter in Book or Report/Conference proceeding › Conference Proceeding › peer-review

9 Citations (Scopus)

Abstract

Feature Ordering is a unique preprocessing step in Incremental Attribute Learning (IAL), where features are gradually trained one after another. In previous studies, feature ordering derived based upon each individual feature's contribution is time-consuming. This study attempts to develop an efficient feature ordering algorithm by some evolutionary approaches. The feature ordering algorithm presented in this paper is based on a criterion of maximum mean of feature discriminability. Experimental results derived by ITID, a neural IAL algorithm, show that such a feature ordering algorithm has a higher probability to obtain the lowest classification error rate with datasets from UCI Machine Learning Repository.

Original language	English
Title of host publication	Network and Parallel Computing - 9th IFIP International Conference, NPC 2012, Proceedings
Pages	482-491
Number of pages	10
DOIs	https://doi.org/10.1007/978-3-642-35606-3_57
Publication status	Published - 2012
Event	9th IFIP International Conference on Network and Parallel Computing, NPC 2012 - Gwangju, Korea, Republic of Duration: 6 Sept 2012 → 8 Sept 2012

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	7513 LNCS
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	9th IFIP International Conference on Network and Parallel Computing, NPC 2012
Country/Territory	Korea, Republic of
City	Gwangju
Period	6/09/12 → 8/09/12

Keywords

Data preprocessing
Feature ordering
Incremental attribute learning
Neural networks
Pattern classification

Access to Document

10.1007/978-3-642-35606-3_57

Cite this

Wang, T., Guan, S. U., Ting, T. O., Man, K. L., & Liu, F. (2012). Evolving linear discriminant in a continuously growing dimensional space for incremental attribute learning. In Network and Parallel Computing - 9th IFIP International Conference, NPC 2012, Proceedings (pp. 482-491). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 7513 LNCS). https://doi.org/10.1007/978-3-642-35606-3_57

Wang, Ting ; Guan, Sheng Uei ; Ting, T. O. et al. / Evolving linear discriminant in a continuously growing dimensional space for incremental attribute learning. Network and Parallel Computing - 9th IFIP International Conference, NPC 2012, Proceedings. 2012. pp. 482-491 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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title = "Evolving linear discriminant in a continuously growing dimensional space for incremental attribute learning",

abstract = "Feature Ordering is a unique preprocessing step in Incremental Attribute Learning (IAL), where features are gradually trained one after another. In previous studies, feature ordering derived based upon each individual feature's contribution is time-consuming. This study attempts to develop an efficient feature ordering algorithm by some evolutionary approaches. The feature ordering algorithm presented in this paper is based on a criterion of maximum mean of feature discriminability. Experimental results derived by ITID, a neural IAL algorithm, show that such a feature ordering algorithm has a higher probability to obtain the lowest classification error rate with datasets from UCI Machine Learning Repository.",

keywords = "Data preprocessing, Feature ordering, Incremental attribute learning, Neural networks, Pattern classification",

author = "Ting Wang and Guan, {Sheng Uei} and Ting, {T. O.} and Man, {Ka Lok} and Fei Liu",

year = "2012",

doi = "10.1007/978-3-642-35606-3_57",

language = "English",

isbn = "9783642356056",

series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",

pages = "482--491",

booktitle = "Network and Parallel Computing - 9th IFIP International Conference, NPC 2012, Proceedings",

note = "9th IFIP International Conference on Network and Parallel Computing, NPC 2012 ; Conference date: 06-09-2012 Through 08-09-2012",

}

Wang, T, Guan, SU, Ting, TO, Man, KL & Liu, F 2012, Evolving linear discriminant in a continuously growing dimensional space for incremental attribute learning. in Network and Parallel Computing - 9th IFIP International Conference, NPC 2012, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 7513 LNCS, pp. 482-491, 9th IFIP International Conference on Network and Parallel Computing, NPC 2012, Gwangju, Korea, Republic of, 6/09/12. https://doi.org/10.1007/978-3-642-35606-3_57

Evolving linear discriminant in a continuously growing dimensional space for incremental attribute learning. / Wang, Ting; Guan, Sheng Uei; Ting, T. O. et al.
Network and Parallel Computing - 9th IFIP International Conference, NPC 2012, Proceedings. 2012. p. 482-491 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 7513 LNCS).

Research output: Chapter in Book or Report/Conference proceeding › Conference Proceeding › peer-review

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AU - Man, Ka Lok

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AB - Feature Ordering is a unique preprocessing step in Incremental Attribute Learning (IAL), where features are gradually trained one after another. In previous studies, feature ordering derived based upon each individual feature's contribution is time-consuming. This study attempts to develop an efficient feature ordering algorithm by some evolutionary approaches. The feature ordering algorithm presented in this paper is based on a criterion of maximum mean of feature discriminability. Experimental results derived by ITID, a neural IAL algorithm, show that such a feature ordering algorithm has a higher probability to obtain the lowest classification error rate with datasets from UCI Machine Learning Repository.

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KW - Feature ordering

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KW - Neural networks

KW - Pattern classification

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Wang T, Guan SU, Ting TO, Man KL, Liu F. Evolving linear discriminant in a continuously growing dimensional space for incremental attribute learning. In Network and Parallel Computing - 9th IFIP International Conference, NPC 2012, Proceedings. 2012. p. 482-491. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-642-35606-3_57

Evolving linear discriminant in a continuously growing dimensional space for incremental attribute learning

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