The analysis of the addition of stochasticity to a neural tree classifier |
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| Affiliation: | 1. Department of Computer Sciences, University of Hertfordshire, Hatfield, Herts AL10 9AB, UK;2. Department of Electrical & Electronics Engineering, Ubonratchathani University, Ubonratchathani 34190, Thailand;1. EEE Department, University of the Ryukyus, 1 Senbaru, Nishihara-cho, Okinawa, Japan;2. Okinawa Radio Observatory, Communications Research Laboratory, 829-3 Daiguskubaru, Aza-kuba, Nakagusuku-son, Okinawa, Japan;3. Institute for Computational Science and Engineering, Ocean University of Qingdao, Shandong, China;1. ISPAIM, National Research Council of Italy, Via Patacca 85, I-80056 Ercolano (NA), Italy;2. Department of Computer Science and Electrical Engineering, University of Salerno, Via Ponte Don Melillo 1, I-84084 Fisciano (SA), Italy |
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| Abstract: | This paper describes various mechanisms for adding stochasticity to a dynamic hierarchical neural clusterer. Such a network grows a tree-structured neural classifier dynamically in response to the unlabelled data with which it is presented. Experiments are undertaken to evaluate the effects of this addition of stochasticity. These tests were carried out using two sets of internal parameters, that define the characteristics of the neural clusterer.A Genetic Algorithm (GA) using appropriate cluster criterion measures in its fitness function was used to search the parameter space for these instantiations. It was found that the addition of non-determinism produced more reliable clustering performances especially on unseen real-world data.Finally, deliberately changing the tree shape by varying key parameters was investigated, illustrated and systematically analysed. |
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