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首页> 外文期刊>Applied Soft Computing >A multi-objective genetic optimization for fast, fuzzy rule-based credit classification with balanced accuracy and interpretability
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A multi-objective genetic optimization for fast, fuzzy rule-based credit classification with balanced accuracy and interpretability

机译：一种多目标遗传优化算法，可实现快速，基于规则的模糊信用分类，并具有均衡的准确性和可解释性

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Credit classification is an important component of critical financial decision making tasks such as credit scoring and bankruptcy prediction. Credit classification methods are usually evaluated in terms of their accuracy, interpretability, and computational efficiency. In this paper, we propose an approach for automatic designing of fuzzy rule-based classifiers (FRBCs) from financial data using multi-objective evolutionary optimization algorithms (MOEOAs). Our method generates, in a single experiment, an optimized collection of solutions (financial FRBCs) characterized by various levels of accuracy-interpretability trade-off. In our approach we address the complexity- and semantics-related interpretability issues, we introduce original genetic operators for the classifier's rule base processing, and we implement our ideas in the context of Non-dominated Sorting Genetic Algorithm II (NSGA-II), i.e., one of the presently most advanced MOEOAs. A significant part of the paper is devoted to an extensive comparative analysis of our approach and 24 alternative methods applied to three standard financial benchmark data sets, i.e., Statlog (Australian Credit Approval), Statlog (German Credit Approval), and Credit Approval (also referred to as Japanese Credit) sets available from the UCI repository of machine learning databases (http://alchive.ics, uci.edu/ml). Several performance measures including accuracy, sensitivity, specificity, and some number of interpretability measures are employed in order to evaluate the obtained systems. Our approach significantly outperforms the alternative methods in terms of the interpretability of the obtained financial data classifiers while remaining either competitive or superior in terms of their accuracy and the speed of decision making. (C) 2015 Elsevier B.V. All rights reserved.
机译：信用分类是关键财务决策任务（例如信用评分和破产预测）的重要组成部分。信用分类方法通常根据其准确性，可解释性和计算效率进行评估。在本文中，我们提出了一种使用多目标进化优化算法（MOEOA）从财务数据自动设计基于模糊规则的分类器（FRBC）的方法。我们的方法在单个实验中生成了优化的解决方案集合（财务FRBC），这些解决方案具有不同级别的精度-可解释性折衷。在我们的方法中，我们解决了与复杂性和语义相关的可解释性问题，为分类器的规则库处理引入了原始遗传算子，并在非支配排序遗传算法II（NSGA-II）的背景下实现了我们的思想，即，是目前最先进的MOEOA之一。本文的很大一部分致力于对我们的方法进行广泛的比较分析，并将24种替代方法应用于三种标准的财务基准数据集，即Statlog（澳大利亚信贷审批），Statlog（德国信贷审批）和Credit审批（也可以从UCI机器学习数据库存储库（http：//alchive.ics，uci.edu/ml）中获得称为“日本信用”的集合。为了评估获得的系统，采用了一些性能指标，包括准确性，敏感性，特异性和一些可解释性指标。在获得的财务数据分类器的可解释性方面，我们的方法明显优于其他方法，同时在准确性和决策速度方面保持竞争力或优越性。 （C）2015 Elsevier B.V.保留所有权利。

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