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International Conference on Discovery Science

DS 2004: Discovery Science pp 155–168Cite as

Resemblance Coefficient and a Quantum Genetic Algorithm for Feature Selection

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Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 3245))

Abstract

Feature selection is always an important and difficult issue in pattern recognition, machine learning and data mining. In this paper, a novel approach called resemblance coefficient feature selection (RCFS) is proposed. Definition, properties of resemblance coefficient (RC) and the evaluation criterion of the optimal feature subset are given firstly. Feature selection algorithm using RC criterion and a quantum genetic algorithm is described in detail. RCFS can decide automatically the minimal dimension of good feature vector and can select the optimal feature subset reliably and effectively. Then the efficient classifiers are designed using neural network. Finally, to bring into comparison, 3 methods, including RCFS, sequential forward selection using distance criterion (SFSDC) and a new method of feature selection (NMFS) presented by Tiejun Lü are used respectively to select the optimal feature subset from original feature set (OFS) composed of 16 features of radar emitter signals. The feature subsets, obtained from RCFS, SFSDC and NMFS, and OFS are employed respectively to recognize 10 typical radar emitter signals in a wide range of signal-to-noise rate. Experiment results show that RCFS not only lowers the dimension of feature vector greatly and simplifies the classifier design, but also achieves higher accurate recognition rate than SFSDC, NMFS and OFS, respectively.

This work was supported by the National Defence Foundation (No.51435030101ZS0502 No.00JSOS.2.1.ZS0501), by the National Natural Science Foundation (No.69574026), by the Doctoral Innovation Foundation of SWJTU and by the Main Teacher Sponsor Program of Education Department of China

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Author information

Authors and Affiliations

  1. National EW Laboratory, 610031, Chengdu, Sichuan, China

    Gexiang Zhang & Laizhao Hu

  2. School of Electrical Engineering, Southwest Jiaotong University, 610031, Chengdu, Sichuan, China

    Gexiang Zhang & Weidong Jin

Authors
  1. Gexiang Zhang
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  2. Laizhao Hu
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  3. Weidong Jin
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Editor information

Editors and Affiliations

  1. Department of Informatics, Graduate School of Information Science and Electrical Engineering, Kyushu University, 744 Motooka, Nishi, 819-0395, Fukuoka, Japan

    Einoshin Suzuki

  2. Kyushu University, 6–10–1 Hakozaki Higashi-ku, 812–8581, Fukuoka, Japan

    Setsuo Arikawa

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© 2004 Springer-Verlag Berlin Heidelberg

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Zhang, G., Hu, L., Jin, W. (2004). Resemblance Coefficient and a Quantum Genetic Algorithm for Feature Selection. In: Suzuki, E., Arikawa, S. (eds) Discovery Science. DS 2004. Lecture Notes in Computer Science(), vol 3245. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-30214-8_12

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  • DOI: https://doi.org/10.1007/978-3-540-30214-8_12

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-23357-2

  • Online ISBN: 978-3-540-30214-8

  • eBook Packages: Springer Book Archive

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