Abstract
Multiple classifier systems (MCS) have become popular during the last decade. Self-generating neural tree (SGNT) is one of the suitable base-classifiers for MCS because of the simple setting and fast learning. However, the computation cost of the MCS increases in proportion to the number of SGNT. In an earlier paper, we proposed a pruning method for the structure of the SGNT in the MCS to reduce the computation cost. In this paper, we propose a novel pruning method for more effective processing and we call this model as self-organizing neural grove (SONG). The pruning method is constructed from an on-line pruning method and an off-line pruning method. Experiments have been conducted to compare the SONG with an unpruned MCS based on SGNT, an MCS based on C4.5, and k-nearest neighbor method. The results show that the SONG can improve its classification accuracy as well as reducing the computation cost.
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Inoue, H., Narihisa, H. (2004). Self-organizing Neural Grove: Efficient Multiple Classifier System Using Pruned Self-generating Neural Trees. In: Yao, X., et al. Parallel Problem Solving from Nature - PPSN VIII. PPSN 2004. Lecture Notes in Computer Science, vol 3242. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-30217-9_112
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DOI: https://doi.org/10.1007/978-3-540-30217-9_112
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