Effects of Three-Objective Genetic Rule Selection on the Generalization Ability of Fuzzy Rule-Based Systems
Effects of Three-Objective Genetic Rule Selection on the Generalization Ability of Fuzzy Rule-Based Systems
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DOI：
10.1007/3-540-36970-8_43
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发表时间：
2003-04
期刊：
影响因子：
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通讯作者：
H. Ishibuchi;Takashi Yamamoto
中科院分区：
文献类型：
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作者：
H. Ishibuchi;Takashi Yamamoto
与经典方法相比，进化多目标优化(EMO)算法的一个优点是可以通过一次运行同时获得多个非支配解。本文展示了如何在遗传规则选择中利用这一优势来设计基于模糊规则的分类系统。我们的遗传规则选择是一个分两个阶段的方法。在第一阶段，使用数据挖掘技术从数值数据中提取预定数量的候选规则。在第二阶段，使用EMO算法从三个目标寻找非支配规则集：最大化正确分类的训练模式数、最小化规则数和最小化规则总长度。由于第一个目标是根据训练模式来衡量的，因此规则集的演变往往过于适合训练模式。问题是，另外两个目标是否起到了防止过度匹配的作用。在本文中，我们通过计算机模拟来检验三目标公式对获得的规则集的泛化能力(即测试模式的分类率)的影响，其中许多非支配规则集是使用EMO算法生成的，用于许多高维模式分类问题。
One advantage of evolutionary multiobjective optimization (EMO) algorithms over classical approaches is that many non-dominated solutions can be simultaneously obtained by their single run. This paper shows how this advantage can be utilized in genetic rule selection for the design of fuzzy rulebased classification systems. Our genetic rule selection is a two-stage approach. In the first stage, a pre-specified number of candidate rules are extracted from numerical data using a data mining technique. In the second stage, an EMO algorithm is used for finding non-dominated rule sets with respect to three objectives: to maximize the number of correctly classified training patterns, to minimize the number of rules, and to minimize the total rule length. Since the first objective is measured on training patterns, the evolution of rule sets tends to overfit to training patterns. The question is whether the other two objectives work as a safeguard against the overfitting. In this paper, we examine the effect of the three-objective formulation on the generalization ability (i.e., classification rates on test patterns) of obtained rule sets through computer simulations where many non-dominated rule sets are generated using an EMO algorithm for a number of high-dimensional pattern classification problems.