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.
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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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References
Zhao, J., Wang, G.Y., Wu, Z.F., et al.: The Study on Technologies for Feature Selection. In: Proc. of 1th Int. Conf. on Machine Learning and Cybernetics, pp. 689–693 (2002)
Molina, L.C., Belanche, L., Nebot, A.: Feature Selection Algorithms: A Survey and Experimental Evaluation. In: Proc. of Int. Conf. on Data Mining, pp. 306–313 (2002)
Guo, G.D., Dyer, C.R.: Simultaneous Selection and Classifier Training Via Linear Programming: A Case Study for Face Expression Recognition. In: Proc. of IEEE Computer Society Conf. on Computer Vision and Pattern Recognition, pp. 346–352 (2003)
Bressan, M., Vitria, J̀.: On the Selection and Classification of Independent Features. IEEE Trans. on Pattern Analysis and Machine Intelligence 25(10), 1312–1317 (2003)
Bian, Z.Q., Zhang, X.G.: Pattern recognition, 2nd edn. Tsinghua University Press, Beijing (2000)
Lü, T.J., Wang, H., Xiao, X.C.: Recognition of Modulation Signal Based on a New Method of Feature Selection. Journal of Electronics and Information Technology 24(5), 661–666 (2002)
Rhee, F.C.H., Lee, Y.J.: Unsupervised feature selection using a fuzzy genetic algorithm. In: Proceedings of 1999 IEEE International Fuzzy Systems Conference, vol. 3, pp. 1266–1269 (1999)
Chakrabarti, S., Dom, B., Agrawal, R., Raghavan, P.: Scalable feature selection, classification and signature generation for organizing large text databases into hierarchical topic taxonomies. The VLDB Jounal 7, 163–178 (1998)
Liu, H.Q., Li, J.Y., Wong, L.S.: A comparative study on feature selection and classification methods using gene expression profiles. Genome Informatics 13, 51–60 (2002)
Thawonmas, R., Abe, S.: A novel approach to feature selection based on analysis of class regions. IEEE Transactions on Systems, Man, and Cybernetics-—Part B: Cybernetics 27(2), 196–207 (1997)
Garrett, D., Peterson, D.A., Anderson, C.W., Thaut, M.H.: Comparison of linear, nonlinear, and feature selection methods for EEG signal classification. IEEE Transactions on Neural Systems and Rehabilitation Engineering 11(2), 141–144 (2003)
Haydar, A., Demirekler, Yurtseven: Speaker identification through use of features selected using genetic algorithm. Electronics Letters 34(1), 39–40 (1998)
Jack, L.B., Nandi, A.K.: Genetic algorithms for feature selection in machine condition monitoring with vibration signals. IEE Proceedings on Vision Image Signal Processing 147(3), 205–212 (2000)
Kwak, N., Choi, C.H.: Input feature selection for classification problems. IEEE Transactions on Neural Networks 13(1), 143–159 (2002)
Zhang, G.X., Jin, W.D., Li, N.: An improved quantum genetic algorithm and its application, Lecture Notes in Artificial Intelligence. In: Wang, G., Liu, Q., Yao, Y., Skowron, A. (eds.) RSFDGrC 2003. LNCS (LNAI), vol. 2639, pp. 449–452. Springer, Heidelberg (2003)
Zhang, G.X., Hu, L.Z., Jin, W.D.: Quantum Computing Based Machine Learning Method and Its Application in Radar Emitter Signal Recognition, August 2004. LNCS (2004) (to appear)
Youssef, H., Sait, S.M., Adiche, H.: Evolutionary algorithms, simulated annealing and tabu search: a comparative study. Engineering Application of Artificial Intellegence 14, 167–181 (2001)
Srinivas, M., Patnaik, L.M.: Genetic algorithms: a survey. Computer 27(6), 17–26 (1994)
Koza, J.R.: Survey of genetic algorithm and genetic programming. In: Proceedings of the 1995 Microelectronics Communications Technology Producing Quality Products Mobile and Portable Power Emerging Technologies, pp. 589–594 (1995)
Han, K.H., Kim, J.H.: Genetic quantum algorithm and its application to combinatorial optimization problems. In: Proceedings of the 2000 IEEE Conference on Evolutionary Computation, pp. 1354–1360 (2000)
Yang, J.A., Li, B., Zhuang, Z.Q.: Research of quantum genetic algorithm and its application in blind source separation. Journal of Electronics 20(1), 62–68 (2003)
Li, Y., Jiao, L.C.: An effective method of image edge detection based on parallel quantum evolutionary algorithm. Signal Processing 19(1), 69–74 (2003)
Zhang, G.P.: Neural Networks for Classification: a Survey. IEEE Trans. on System, Man, and Cybernetics-Part C: Application and Reviews. 30(4), 451–462 (2000)
Kavalov, D., Kalinin, V.: Neural Network Surface Acoustic Wave RF Signal Processor for Digital Modulation Recognition. IEEE Trans. on Ultrasonics, Ferroelectrics, and Frequency Control 49(9), 1280–1290 (2002)
Riedmiller, M., Braun, H.: A Direct Adaptive Method for Faster Back Propagation Learning: The RPROP Algorithm. In: Proc. of IEEE Int. Conf. on Neural Networks, pp. 586–591 (1993)
Zhang, G.X., Jin, W.D., Hu, L.Z.: Fractal Feature Extraction of Radar Emitter Signals. In: Proc. of the third Asia-Pacific conf. on Environmental Electromagnetics, pp. 161–164 (2003)
Zhang, G.X., Hu, L.Z., Jin, W.D.: Complexity Feature Extraction of Radar Emitter Signals. In: Proc. of the third Asia-Pacific Conf. on Environmental Electromagnetics, pp. 495–498 (2003)
Zhang, G.X., Rong, H.N., Jin, W.D., Hu, L.Z.: Radar emitter signal recognition based on resemblance coefficient features. In: Tsumoto, S., Słowiński, R., Komorowski, J., Grzymała-Busse, J.W. (eds.) RSCTC 2004. LNCS (LNAI), vol. 3066, pp. 665–670. Springer, Heidelberg (2004)
Zhang, G.X., Rong, H.N., Hu, L.Z., Jin, W.D.: Entropy Feature Extraction Approach of Radar Emitter Signals. In: Proceedings of International Conference on Intelligent Mechatronics and Automation (August 2004) (to appear)
Zhang, G.X., Jin, W.D., Hu, L.Z.: Application of Wavelet Packet Transform to Signal Recognition. In: Proceedings of International Conference on Intelligent Mechatronics and Automation (August 2004) (to appear)
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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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