C.S. Leung, M. Lee, and J.H. Chan (Eds.): ICONIP 2009, Part II, LNCS 5864, pp. 611–619, 2009. © Springer-Verlag Berlin Heidelberg 2009 String Pattern Recognition Using Evolving Spiking Neural Networks and Quantum Inspired Particle Swarm Optimization Haza Nuzly Abdull Hamed 1 , Nikola Kasabov 1 , Zbynek Michlovský 2 , and Siti Mariyam Shamsuddin 3 1 Knowledge Engineering and Discovery Research Institute (KEDRI), Auckland University of Technology, New Zealand {hnuzly,nkasabov}@aut.ac.nz 2 Faculty of Information Technology, Brno University of Technology, Brno, Czech Republic imichlov@fit.vutbr.cz 3 Soft Computing Research Group, Universiti Teknologi Malaysia, Malaysia mariyam@utm.my Abstract. This paper proposes a novel method for string pattern recognition us- ing an Evolving Spiking Neural Network (ESNN) with Quantum-inspired Parti- cle Swarm Optimization (QiPSO). This study reveals an interesting concept of QiPSO by representing information as binary structures. The mechanism opti- mizes the ESNN parameters and relevant features using the wrapper approach simultaneously. The N-gram kernel is used to map Reuters string datasets into high dimensional feature matrix which acts as an input to the proposed method. The results show promising string classification results as well as satisfactory QiPSO performance in obtaining the best combination of ESNN parameters and in identifying the most relevant features. Keywords: String Kernels, Text Classification, Evolving Spiking Neural Net- work, Particle Swarm, Quantum Computing. 1 Introduction String pattern recognition is an approach for determining which group a string belongs to, according to its contents. This task, despite being quite challenging, is very impor- tant to certain areas such as internet security and virus detection. Strings can be texts, musical symbols or others which are not necessarily in numerical formats. Since most classifier algorithms can only accept numerical values, transformation from strings to numerical values is required. String kernels are a well-known method to transform string input values into high dimensional input vectors. There are several well-known string kernels such as Bag of Words (BOW) and N-gram Kernels. Output from the kernel process which is the kernel matrix is used as an input to the algorithm for classi- fication, clustering or ranking tasks. This technique is quite simple yet quite effective to transform input from string to the numerical value. KEDRI-NICT Project Report - APPENDIX:E