文章基本信息

标题：IMPROVED SELF-ORGANIZING MAPS BASED ON DISTANCE TRAVELLED BY NEURONS
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作者：HICHAM OMARA ; MOHAMED LAZAAR ; YOUNESS TABII 等
期刊名称：Journal of Theoretical and Applied Information Technology
印刷版ISSN：1992-8645
电子版ISSN：1817-3195
出版年度：2018
卷号：96
期号：5
出版社：Journal of Theoretical and Applied
摘要：The Self-Organizing Map (SOM) is a commonly algorithm used for visualizing and classification of datasets, due to its ability to project high-dimensional data in a lower dimension. However, certain topological constraints of the SOM are fixed before the learning phase; the appropriate number of neurons has a major influence on the classification accuracy. Many researchers have tried to deal with this problem. This paper presents a novel approach to improve SOM based on distance travelled by each neuron. This approach is testing on two different databases of breast cancer. The model will classify the input vectors into two classes of cancer type (benign and malignant); the result obtained shows amelioration compared to classical SOM; up to 2% of improvement in classification accuracy is observed. We can conclude that our approach seems an efficient method in medical applications and especially for the cancer classification.
关键词：Self-Organizing Maps; Weighting Optimization; Classification; Cancer