Improving multi-objective clustering through support vector machine: Application to gene expression data

A. Mukhopadhyay, U. Maulik, S. Bandyopadhyay
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引用次数: 1

Abstract

Microarray technology facilitates the monitoring of the expression profile of a large number of genes across different experimental conditions simultaneously. This article proposes a novel approach that combines a recently proposed multiobjective fuzzy clustering scheme with support vector machine (SVM), to yield improved solutions. The multiobjective technique is first used to produce a set of non-dominated solutions. The non-dominated set is then used to find some high-confidence points using a fuzzy voting technique. The SVM classifier is trained by this high-confidence points. Finally the remaining points are classified using the trained classifier. Results demonstrating the effectiveness of the proposed technique are provided for three real life gene expression data sets. Moreover statistical significance test has been conducted to establish the significant superiority of the proposed technique.
支持向量机改进多目标聚类:在基因表达数据中的应用
微阵列技术有助于同时监测不同实验条件下大量基因的表达谱。本文提出了一种新的方法,将最近提出的多目标模糊聚类方案与支持向量机(SVM)相结合,以产生改进的解。多目标技术首先用于生成一组非支配解。然后使用模糊投票技术使用非支配集来找到一些高置信度点。SVM分类器通过这些高置信度点进行训练。最后使用训练好的分类器对剩余的点进行分类。结果证明了所提出的技术的有效性提供了三个现实生活中的基因表达数据集。此外,还进行了统计显著性检验,以确定所提出的技术的显著优越性。
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