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Application of a rule extraction algorithm family based on the re-rx algorithm to financial credit risk assessment from a Pareto optimal perspective

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OPERATIONS reSEARCH PERSPECTIVES 2016年 3卷 32-42页

作者： Hayashi, Yoichi Meiji Univ Dept Comp Sci Tama Ku Kawasaki Kanagawa 2148571 Japan

Historically, the assessment of credit risk has proved to be both highly important and extremely difficult. Currently, financial institutions rely on the use of computer-generated credit scores for risk assessment. However, automated risk evaluations are currently imperfect, and the loss of vast amounts of capital could be prevented by improving the performance of computerized credit assessments. A number of approaches have been developed for the computation of credit scores over the last several decades, but these methods have been considered too complex without good interpretability and have therefore not been widely adopted. Therefore, in this study, we provide the first comprehensive comparison of results regarding the assessment of credit risk obtained using 10 runs of 10-fold cross validation of the re-rx algorithm family, including the re-rx algorithm, the re-rx algorithm with both discrete and continuous attributes (Continuous re-rx), the re-rx algorithm with J48graft, the re-rx algorithm with a trained neural network (Sampling re-rx), NeuroLinear, NeuroLinear + GRG, and three unique rule extraction techniques involving support vector machines and Minerva from four real-life, two-class mixed credit-risk datasets. We also discuss the roles of various newly-extended types of the re-rx algorithm and high performance classifiers from a Pareto optimal perspective. Our findings suggest that Continuous re-rx, re-rx with J48graft, and Sampling re-rx comprise a powerful management tool that allows the creation of advanced, accurate, concise and interpretable decision support systems for credit risk evaluation. In addition, from a Pareto optimal perspective, the re-rx algorithm family has superior features in relation to the comprehensibility of extracted rules and the potential for credit scoring with Big Data. (C) 2016 The Authors. Published by Elsevier Ltd.

关键词： Credit risk assessment Credit scoring Rule extraction Pareto optimal re-rx algorithm Financial application

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Strategic Approach for Multiple-MLP Ensemble re-rx algorithm

Strategic Approach for Multiple-MLP Ensemble Re-RX Algorithm

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International Joint Conference on Neural Networks (IJCNN)

作者： Hayashi, Yoichi Fujisawa, Shota Meiji Univ Dept Comp Sci Tama Ku Kawasaki Kanagawa 2148571 Japan

ISBN: (纸本)9781479919598

In this paper, we review all our work since 2012 and propose a strategic approach for the Multiple-MLP Ensemble rerx algorithm. We first describe the background and procedures of the recursive-Rule Extraction (re-rx) algorithm family and its variants, including the Multiple-MLP Ensemble re-rx algorithm ("Multiple-MLP Ensemble"), which uses the re-rx algorithm as its core. The proposed strategic approach consists of two processes: non-pruning for the trained neural network ensembles without continuous attributes and a relaxed rule generation scheme using continuous attributes to extract extremely accurate, comprehensible, and concise rules for multi-class mixed datasets (i.e., discrete attributes and continuous attributes). We conducted experiments to find rules for seven kinds of multi-class mixed datasets and compared the accuracy, comprehensibility, and conciseness for the Multiple-MLP Ensemble re-rx algorithm. The strategic approach for the Multiple-MLP Ensemble re-rx algorithm outperformed the original Multiple-MLP Ensemble rerx algorithm. These results confirm that the strategic approach for the Multiple-MLP Ensemble algorithm facilitates the migration from existing data systems toward new accurate analytic systems and Big Data.

关键词： re-rx algorithm Rule Extraction MLP Data Mining Big Data Neural Network Ensemble Mixed Dataset Ensemble Concept Non-pruning relaxed rule generation Scheme

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Three-MLP Ensemble re-rx algorithm and recent Classifiers for Credit-Risk Evaluation

Three-MLP Ensemble Re-RX Algorithm and Recent Classifiers fo...

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International Joint Conference on Neural Networks (IJCNN)

作者： Hayashi, Yoichi Tanaka, Yuki Yukita, Shonosuke Nakano, Satoshi Bologna, Guido Meiji Univ Dept Comp Sci Tama Ku Kawasaki Kanagawa 2148571 Japan Univ Appl Sci Western Switzerland Dept Informat Technol CH-1204 Geneva Switzerland

ISBN: (纸本)9781479919598

Credit-risk evaluation is a challenging and important task in the domain of financial analysis for which many classification methods have been suggested. In this paper, we present the results for eight real-life credit-risk two-class mixed datasets (i.e., discrete and continuous attributes) analyzed by the Three-MLP Ensemble re-rx algorithm (shortened to "Three-MLP Ensemble"). Clarifying the neural network decisions by explanatory rules that capture the learned knowledge embedded in the networks can help a credit-risk manager explain why a particular applicant is classified as either bad or good. To compare the Three-MLP Ensemble performance, we executed comprehensive rule extraction experiments on eight two-class mixed datasets commonly used for benchmarking studies in credit-risk evaluation. The extremely high accuracy of the Three-MLP Ensemble outperformed the accuracies by the re-rx algorithm and a variant. In this study, we also compared the accuracy of the Three-MLP Ensemble with that of classifiers recently proposed. It is concluded that neural network rule extraction by the Three-MLP Ensemble is a powerful management tool that allows us to build advanced, comprehensible, and accurate decision-support systems for credit-risk evaluation.

关键词： credit score credit-risk evaluation re-rx algorithm rule extraction MLP data mining Big Data neural network ensemble mixed dataset ensemble concept

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Rule Extraction from Neural Network Using Input Data Ranges recursively

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NEW GENERATION COMPUTING 2019年第1期37卷 67-96页

作者： Chakraborty, Manomita Biswas, Saroj Kumar Purkayastha, Biswajit Natl Inst Technol Silchar Comp Sci & Engn Dept Silchar 788010 Assam India

Neural network is one of the best tools for data mining tasks due to its high accuracy. However, one of the drawbacks of neural network is its black box nature. This limitation makes neural network useless for many applications which require transparency in their decision-making process. Many algorithms have been proposed to overcome this drawback by extracting transparent rules from neural network, but still researchers are in search foralgorithms that can generatemore accurate and simplerules. Therefore, this paper proposes a rule extraction algorithm named Eclectic Rule Extraction from Neural Network recursively (EreNNR), with the aim to generate simple and accurate rules. EreNNR algorithmextracts symbolic classification rules from a single-layer feed-forward neural network. The novelty of this algorithm lies in its procedure of analyzing the nodes of the network. It analyzes a hidden node based on data ranges of input attributes with respect to its output and analyzes an output node using logical combination of the outputs of hidden nodes with respect to output class. And finally it generates a rule set by proceeding in a backward direction starting from the output layer. For each rule in the set, it repeats the whole process of rule extraction if the rule satisfies certain criteria. The algorithm is validated with eleven benchmark datasets. Experimental results show that the generated rules are simple and accurate.

关键词： Neural network Data mining Rule extraction Classification re-rx algorithm rxreN algorithm

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recursive Rule Extraction from NN using reverse Engineering Technique

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NEW GENERATION COMPUTING 2018年第2期36卷 119-142页

作者： Chakraborty, Manomita Biswas, Saroj Kr. Purkayastha, Biswajit Natl Inst Technol Silchar Comp Sci & Engn Dept Silchar 788010 Assam India

This paper proposes an algorithm named reverse Engineering recursive Rule Extraction (re-re-rx) for symbolic rule extraction from neural network with mixed attributes. re-re-rx algorithm is an extension of the existing recursive Rule Extraction (re-rx) algorithm. re-rx algorithm generates disjoint rules for continuous and discrete attributes. The algorithm first generates rules for discrete attributes. A rule for discrete attributes is further refined recursively if it does not produce satisfactory result. A rule is refined by generating rules with the discrete attributes (if present) that are not covered by the rule or else the process is terminated by generating rules with the continuous attributes (if present). The novelty of the proposed re-re-rx algorithm lies in generating rules for continuous attributes. re-rx generates linear hyper plane for continuous attributes which may not be able to deal with the non-linearity present in data. To overcome this limitation re-re-rx algorithm generates simple rules for continuous attributes in the form of input data ranges and target. re-re-rx uses the concept of Rule Extraction by reverse Engineering the NN (rxreN) algorithm in slightly different way to generate rules. rxreN only uses misclassified patterns, whereas re-re-rx uses both classified and misclassified patterns of each continuous attribute to calculate input data ranges for constructing rules. The proposed algorithm is validated with six benchmark datasets. The experimental results clearly show the superiority of the proposed algorithm to re-rx.

关键词： Data mining Neural networks Rule extraction re-rx algorithm rxreN algorithm Classification

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Ensemble Neural Network Rule Extraction Using re-rx algorithm

Ensemble Neural Network Rule Extraction Using Re-RX Algorith...

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International Joint Conference on Neural Networks (IJCNN)

作者： Hara, Atsushi Hayashi, Yoichi Fujitsu Social Sci Lab Ltd Nakahara Ku Kawasaki Kanagawa 2110063 Japan Meiji Univ Dept Comp Sci Kawasaki Kanagawa 2148571 Japan

ISBN: (纸本)9781467314909

In this paper, we propose a feed-forward ensemble neural network for data sets having both discrete and continuous attributes. The ensemble provides results that are more accurate than those of conventional neural networks and expresses more comprehensible rules. Through the separation of data in compliance with primary rules, it enables the generation of secondary rules that apply solely to instances of non-compliance with the primary rules and maintain higher accuracy than is conventionally attainable. We demonstrate the high performance of the ensemble neural network with rules extracted by re-rx, and verify that it can reduce the complexity of handling multiple neural networks.

关键词： Ensemble neural network rule extraction re-rx algorithm Ensemble method recursive neural network rule extraction

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Accuracy of rule extraction using a recursive-rule extraction algorithm with continuous attributes combined with a sampling selection technique for the diagnosis of liver disease

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Informatics in Medicine Unlocked 2016年 5卷 26-38页

作者： Hayashi, Yoichi Fukunaga, Kazuhiro Department of Computer Science Meiji University Tama-ku Kawasaki 214-8571 Kanagawa Japan

Although liver cancer is the second most common cause of death from cancer worldwide, because of the limited accuracy and interpretability of extracted classification rules, the diagnosis of liver disease remains difficult. In addition, hepatitis, which is inflammation of the liver, can progress to fibrosis, cirrhosis, or even liver cancer. Numerous methods for diagnosing liver disease have been applied, but most current diagnostic methods are black box models that cannot adequately reveal information hidden in the data. In the medical setting, extracted rules must be not only highly accurate, but also highly interpretable. The recursive-Rule eXtraction (re-rx) algorithm is a white box model that generates highly accurate and interpretable classification rules on the basis of both discrete and continuous attributes;however, it tends to generate more rules than other rule extraction algorithms. The objectives of this study were to use a new rule extraction algorithm, Continuous re-rx combined with sampling selection techniques (Sampling-Continuous re-rx), to achieve highly accurate and interpretable diagnostic rules for the BUPA and Hepatitis datasets and to quantify the associations between the presence and severity of ascites and serum biomarkers with the risk of developing hepatitis in consideration of Child-Pugh scores. The performance of Sampling-Continuous re-rx was compared with existing techniques, and as a result, it was found to extract more accurate, concise, and interpretable rules for the BUPA and Hepatitis datasets compared with previous extraction algorithms. In addition, the rules extracted using the proposed method were close to the trade-off curve, which indicated that they were more accurate and interpretable, and therefore more suitable in the medical setting. © 2016

关键词： BUPA liver disorder dataset Child-Pugh score Hepatitis dataset re-rx algorithm Rule extraction Sampling selection technique

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Rule extraction using recursive-Rule extraction algorithm with J48graft combined with sampling selection techniques for the diagnosis of type 2 diabetes mellitus in the Pima Indian dataset

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Informatics in Medicine Unlocked 2016年 2卷 92-104页

作者： Hayashi, Yoichi Yukita, Shonosuke Department of Computer Science Meiji University Tama-ku Kawasaki 214-8571 Kanagawa Japan

Diabetes is a complex disease that is increasing in prevalence around the world. Type 2 diabetes mellitus (T2DM) accounts for about 90–95% of all diagnosed adult cases of diabetes. Most present diagnostic methods for T2DM are black-box models, which are unable to provide the reasons underlying diagnosis to physicians;therefore, algorithms that can provide further insight are needed. Rule extraction can provide such explanations;however, in the medical setting, extracted rules must be not only highly accurate, but also simple and easy to understand. The recursive-Rule eXtraction (re-rx) algorithm is a “white-box” model that provides highly accurate classification. However, due to its recursive nature, it tends to generate more rules than other algorithms. Therefore, in this study, we propose the use of a rule extraction algorithm, re-rx with J48graft, combined with sampling selection techniques (sampling re-rx with J48graft) to achieve highly accurate, concise, and interpretable classification rules for the Pima Indian Diabetes (PID) dataset, which comprises 768 samples with two classes (diabetes or non-diabetes) and eight continuous attributes. The use of this algorithm resulted in an average accuracy of 83.83% after 10 runs of 10-fold cross validation. Sampling re-rx with J48 graft achieved substantially better accuracy and provided a considerably fewer average number of rules and antecedents than the original re-rx algorithm. These results suggest that sampling re-rx with J48graft provides more accurate, concise, and interpretable extracted rules than previous algorithms, and is therefore more suitable for medical decision making, including the diagnosis of T2DM. © 2016

关键词： Data mining Pima Indian diabetes re-rx algorithm Rule extraction Sampling selection Type 2 diabetes mellitus

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Use of the recursive-rule extraction algorithm with continuous attributes to improve diagnostic accuracy in thyroid disease

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Informatics in Medicine Unlocked 2015年 1卷 1-8页

作者： Hayashi, Yoichi Nakano, Satoshi Fujisawa, Shota Department of Computer Science Meiji University Tama-ku Kawasaki 214-8571 Kanagawa Japan

Thyroid diseases, which often lead to thyroid dysfunction involving either hypo- or hyperthyroidism, affect hundreds of millions of people worldwide, many of whom remain undiagnosed;however, diagnosis is difficult because symptoms are similar to those seen in a number of other conditions. The objective of this study was to assess the effectiveness of the recursive-Rule Extraction (re-rx) algorithm with continuous attributes (Continuous re-rx) in extracting highly accurate, concise, and interpretable classification rules for the diagnosis of thyroid disease. We used the 7200-sample Thyroid dataset from the University of California Irvine Machine Learning repository, a large and highly imbalanced dataset that comprises both discrete and continuous attributes. We trained the dataset using Continuous re-rx, and after obtaining the maximum training and test accuracies, the number of extracted rules, and the average number of antecedents, we compared the results with those of other extraction methods. Our results suggested that Continuous re-rx not only achieved the highest accuracy for diagnosing thyroid disease compared with the other methods, but also provided simple, concise, and interpretable rules. Based on these results, we believe that the use of Continuous re-rx in machine learning may assist healthcare professionals in the diagnosis of thyroid disease. © 2016

关键词： Decision tree re-rx algorithm Rule extraction Thyroid disease diagnosis

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Use of a recursive-Rule eXtraction algorithm with J48graft to achieve highly accurate and concise rule extraction from a large breast cancer dataset

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Informatics in Medicine Unlocked 2015年 1卷 9-16页

作者： Hayashi, Yoichi Nakano, Satoshi Department of Computer Science Meiji University Tama-ku 214-8571 Kanagawa Japan

To assist physicians in the diagnosis of breast cancer and thereby improve survival, a highly accurate computer-aided diagnostic system is necessary. Although various machine learning and data mining approaches have been devised to increase diagnostic accuracy, most current methods are inadequate. The recently developed recursive-Rule eXtraction (re-rx) algorithm provides a hierarchical, recursive consideration of discrete variables prior to analysis of continuous data, and can generate classification rules that have been trained on the basis of both discrete and continuous attributes. The objective of this study was to extract highly accurate, concise, and interpretable classification rules for diagnosis using the re-rx algorithm with J48graft, a class for generating a grafted C4.5 decision tree. We used the Wisconsin Breast Cancer Dataset (WBCD). Nine research groups provided 10 kinds of highly accurate concrete classification rules for the WBCD. We compared the accuracy and characteristics of the rule set for the WBCD generated using the re-rx algorithm with J48graft with five rule sets obtained using 10-fold cross validation (CV). We trained the WBCD using the re-rx algorithm with J48graft and the average classification accuracies of 10 runs of 10-fold CV for the training and test datasets, the number of extracted rules, and the average number of antecedents for the WBCD. Compared with other rule extraction algorithms, the re-rx algorithm with J48graft resulted in a lower average number of rules for diagnosing breast cancer, which is a substantial advantage. It also provided the lowest average number of antecedents per rule. These features are expected to greatly aid physicians in making accurate and concise diagnoses for patients with breast cancer. © 2016

关键词： Breast cancer diagnosis C4.5 J48graft re-rx algorithm Rule extraction

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