大会名称 |
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2018年 情報科学技術フォーラム(FIT) |
大会コ-ド |
F |
開催年 |
2018 |
発行日 |
2018-09-12 |
セッション番号 |
6f |
セッション名 |
機械学習(4) |
講演日 |
2018/09/21 |
講演場所(会議室等) |
D棟D23 |
講演番号 |
IF-002 |
タイトル |
Selg-Organizing Neural Grove: SONG |
著者名 |
井上浩孝, |
キーワード |
Ensemble Learning, Improving Generalization Capability |
抄録 |
Recently, deep learning neural networks have been used for practical applications to improve classification accuracy. However, the training time of the deep learning neural networks increases in proportion to the number of layers. On the other hand, the training time of multiple classifier systems (MCS) based on self-generating neural trees extremely quick. In this paper, we propose a novel pruning method for efficient classification for the neural network ensembles and we call this model a self-organizing neural grove (SONG). Experiments have been conducted to compare the pruned MCS with an unpruned MCS, the 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 not only toy problems, but also practical problems. |
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