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A study on the application of instance selection techniques in genetic fuzzy rule-based classification systems: Accuracy-complexity trade-off

机译：实例选择技术在基于遗传模糊规则的分类系统中的应用研究：精度-复杂度的权衡

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摘要

In the framework of genetic fuzzy systems, the computational time required by genetic algorithms for generating fuzzy rule-based models from data increases considerably with the increase of the number of instances in the training set, mainly due to the fitness evaluation. Also, the amount of data typically affects the complexity of the resulting model: a higher number of instances generally induces the generation of models with a higher number of rules. Since the number of rules is considered one of the factors which affect the interpretability of the fuzzy rule-based models, large datasets generally bring to less interpretable models. Both these problems can be tackled and partially solved by reducing the number of instances before applying the evolutionary process. In the literature several algorithms of instance selection have been proposed for selecting instances without deteriorating the accuracy of the generated models. The aim of this paper is to analyze the effectiveness of 36 training set selection methods when combined with genetic fuzzy rule-based classification systems. Using 37 datasets of different sizes we show that some of these methods can considerably help to reduce the computational time of the evolutionary process and to decrease the complexity of the fuzzy rule-based models with a very limited decrease of their accuracy with respect to the models generated by using the overall training set.
机译：在遗传模糊系统的框架中，遗传算法从数据生成基于模糊规则的模型所需的计算时间随着训练集中实例数量的增加而显着增加，这主要是由于适应性评估所致。同样，数据量通常会影响所得模型的复杂性：实例数量更多通常会导致生成规则数量更多的模型。由于规则的数量被认为是影响基于模糊规则的模型的可解释性的因素之一，因此大型数据集通常会带来难以解释的模型。通过应用进化过程之前减少实例数量，可以解决和部分解决这两个问题。在文献中，已经提出了几种实例选择算法，用于选择实例而不会降低所生成模型的准确性。本文的目的是分析与基于遗传模糊规则的分类系统结合使用的36种训练集选择方法的有效性。使用37个不同大小的数据集，我们表明这些方法中的某些方法可以极大地帮助减少进化过程的计算时间，并降低基于模糊规则的模型的复杂性，并且相对于模型，其准确性的降低非常有限通过使用整体训练集生成。

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