Entropic feature discrimination ability for pattern classification based on neural IAL

Ting Wang; Sheng Uei Guan; Fei Liu

doi:10.1007/978-3-642-31362-2_4

Entropic feature discrimination ability for pattern classification based on neural IAL

Ting Wang^*, Sheng Uei Guan, Fei Liu

^*Corresponding author for this work

Department of Computing

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

8 Citations (Scopus)

Abstract

Incremental Attribute Learning (IAL) is a novel machine learning strategy, where features are gradually trained in one or more according to some orderings. In IAL, feature ordering is a special preprocessing. Apart from time-consuming contribution-based feature ordering methods, feature ordering also can be derived by filter criteria. In this paper, a novel criterion based on Discriminability, a distribution-based metric, and Entropy is presented to give ranks for feature ordering, which has been validated in both two-category and multivariable classification problems by neural networks. Final experimental results show that the new metric is not only applicable for IAL, but also able to obtain better performance in lower error rates.

Original language	English
Title of host publication	Advances in Neural Networks, ISNN 2012 - 9th International Symposium on Neural Networks, Proceedings
Pages	30-37
Number of pages	8
Edition	PART 2
DOIs	https://doi.org/10.1007/978-3-642-31362-2_4
Publication status	Published - 2012
Event	9th International Symposium on Neural Networks, ISNN 2012 - Shenyang, China Duration: 11 Jul 2012 → 14 Jul 2012

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Number	PART 2
Volume	7368 LNCS
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	9th International Symposium on Neural Networks, ISNN 2012
Country/Territory	China
City	Shenyang
Period	11/07/12 → 14/07/12

Keywords

discrimination ability
entropy
feature ordering
incremental attribute learning
neural networks

Access to Document

10.1007/978-3-642-31362-2_4

Cite this

Wang, T., Guan, S. U., & Liu, F. (2012). Entropic feature discrimination ability for pattern classification based on neural IAL. In Advances in Neural Networks, ISNN 2012 - 9th International Symposium on Neural Networks, Proceedings (PART 2 ed., pp. 30-37). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 7368 LNCS, No. PART 2). https://doi.org/10.1007/978-3-642-31362-2_4

Wang, Ting ; Guan, Sheng Uei ; Liu, Fei. / Entropic feature discrimination ability for pattern classification based on neural IAL. Advances in Neural Networks, ISNN 2012 - 9th International Symposium on Neural Networks, Proceedings. PART 2. ed. 2012. pp. 30-37 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); PART 2).

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title = "Entropic feature discrimination ability for pattern classification based on neural IAL",

abstract = "Incremental Attribute Learning (IAL) is a novel machine learning strategy, where features are gradually trained in one or more according to some orderings. In IAL, feature ordering is a special preprocessing. Apart from time-consuming contribution-based feature ordering methods, feature ordering also can be derived by filter criteria. In this paper, a novel criterion based on Discriminability, a distribution-based metric, and Entropy is presented to give ranks for feature ordering, which has been validated in both two-category and multivariable classification problems by neural networks. Final experimental results show that the new metric is not only applicable for IAL, but also able to obtain better performance in lower error rates.",

keywords = "discrimination ability, entropy, feature ordering, incremental attribute learning, neural networks",

author = "Ting Wang and Guan, {Sheng Uei} and Fei Liu",

year = "2012",

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language = "English",

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

number = "PART 2",

pages = "30--37",

booktitle = "Advances in Neural Networks, ISNN 2012 - 9th International Symposium on Neural Networks, Proceedings",

edition = "PART 2",

note = "9th International Symposium on Neural Networks, ISNN 2012 ; Conference date: 11-07-2012 Through 14-07-2012",

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Wang, T, Guan, SU & Liu, F 2012, Entropic feature discrimination ability for pattern classification based on neural IAL. in Advances in Neural Networks, ISNN 2012 - 9th International Symposium on Neural Networks, Proceedings. PART 2 edn, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), no. PART 2, vol. 7368 LNCS, pp. 30-37, 9th International Symposium on Neural Networks, ISNN 2012, Shenyang, China, 11/07/12. https://doi.org/10.1007/978-3-642-31362-2_4

Entropic feature discrimination ability for pattern classification based on neural IAL. / Wang, Ting; Guan, Sheng Uei; Liu, Fei.
Advances in Neural Networks, ISNN 2012 - 9th International Symposium on Neural Networks, Proceedings. PART 2. ed. 2012. p. 30-37 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 7368 LNCS, No. PART 2).

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

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AU - Liu, Fei

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AB - Incremental Attribute Learning (IAL) is a novel machine learning strategy, where features are gradually trained in one or more according to some orderings. In IAL, feature ordering is a special preprocessing. Apart from time-consuming contribution-based feature ordering methods, feature ordering also can be derived by filter criteria. In this paper, a novel criterion based on Discriminability, a distribution-based metric, and Entropy is presented to give ranks for feature ordering, which has been validated in both two-category and multivariable classification problems by neural networks. Final experimental results show that the new metric is not only applicable for IAL, but also able to obtain better performance in lower error rates.

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KW - neural networks

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Wang T, Guan SU, Liu F. Entropic feature discrimination ability for pattern classification based on neural IAL. In Advances in Neural Networks, ISNN 2012 - 9th International Symposium on Neural Networks, Proceedings. PART 2 ed. 2012. p. 30-37. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); PART 2). doi: 10.1007/978-3-642-31362-2_4

Entropic feature discrimination ability for pattern classification based on neural IAL

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Keywords

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