演化多项式-模糊分类模型的可理解性诱导

E. Mugambi, A. Hunter
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引用次数: 0

摘要

可理解性是医学预测建模的一个重要因素,因为它决定了模型的可信度甚至可接受性。通常,模型的性能一直是大多数数据挖掘工作的主要关注点。如果模型所做的决策带来了严重的风险,那么将模型的可理解性方面视为性能的次要方面是不可行的。虽然模型可理解性是一个引起了很多人兴趣的话题,两个会议研讨会(AI-UCAI'95和AAAI 2005)将其作为主题问题,许多论文都写了关于它的文章,但没有测量它的经验方法,甚至没有一个一致的方法来定义它。人们普遍认为,较小的模型比较大的模型更容易理解。这构成了在这个领域进行的大多数研究的基础。本文研究了在Pareto意义下使用多目标优化来满足模型可理解性要求的有效性。本文中使用的目标函数有些是新的,有些则是在其他研究中使用过的。结果表明,在归纳过程模型中加入可理解性并不一定会降低模型的性能,实际上可以提高进化多项式-模糊结构的性能与复杂性之间的权衡
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Inducing Comprehensibility In Evolutionary Polynomial-Fuzzy Classification Models
Comprehensibility is an important factor in medical predictive modelling as it dictates the credibility and even acceptability of a model. Generally, the performance of a model has always been the primary focus in most data mining jobs. Where there are serious risks posed by the decisions made by a model, it is not feasible to view comprehensibility aspects of a model as secondary to performance. While model comprehensibility is a topic that has aroused a lot of interest with two conference workshops (AI-UCAI'95 & AAAI 2005) placing it as its keynote issue and many papers written about it, there are no empirical methods of measuring it or even one consistent way to define it. It is generally accepted that smaller models are more comprehensible than larger ones. This forms the basis of most researches conducted in this area. In this paper, we investigate the efficacy of using multiobjective optimization in the Pareto sense to meet comprehensibility demands of models. Some of the objective functions used in this paper are novel while others have been used in other researches before. The results obtained show that incorporating aspects of comprehensibility in the induction process models does not necessarily retard the performance of models and could actually improve the performance versus complexity trade-off of evolutionary polynomial-fuzzy structures
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