The Use of Genetic Algorithm, Clustering and Feature Selection Techniques in Construction of Decision Tree Models for Credit Scoring

Khanbabaei, Mohammad; Alborzi, Mahmood

doi:10.5121/ijmit.2013.5402

Cited by 10 publications

(6 citation statements)

References 59 publications

(75 reference statements)

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“…wrapper approach with Naïve Bayes, MLP, RBF neural network, SVM, Random Forest, Linear Discriminant classifier and Nearest Mean classifier for feature selection for credit rating prediction [53]; the combination of a clustering algorithm and GA with Decision Tree for feature selection for credit scoring of customers [29]; a hybrid approach for credit risk assessment using GA and ANN to obtain an optimum set of features to improve the classification accuracy and scalability [54]; a GA with weighted bitmask as alternative of polynomial fitness functions to estimate parameter range for building credit scoring models [54]; parallelisation of Random Forest method and feature selection methods, such as filters (t-test, LR, LDA), wrappers (GA, PSO) in credit scoring models [55].…”

Section: Existing Workmentioning

confidence: 99%

Information gain directed genetic algorithm wrapper feature selection for credit rating

Jadhav

Jenkins

2018

Applied Soft Computing

220

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Financial credit scoring is one of the most crucial processes in the finance industry sector to be able to assess the credit-worthiness of individuals and enterprises. Various statistics-based machine learning techniques have been employed for this task. "Curse of Dimensionality" is still a significant challenge in machine learning techniques. Some research has been carried out on Feature Selection (FS) using genetic algorithm as wrapper to improve the performance of credit scoring models. However, the challenge lies in finding an overall best method in credit scoring problems and improving the time-consuming process of feature selection. In this study, the credit scoring problem is investigated through feature selection to improve classification performance. This work proposes a novel approach to feature selection in credit scoring applications, called as Information Gain Directed Feature Selection algorithm (IGDFS), which performs the ranking of features based on information gain, propagates the top m features through the GA wrapper (GAW) algorithm using

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Section: Existing Workmentioning

confidence: 99%

Information gain directed genetic algorithm wrapper feature selection for credit rating

Jadhav

Jenkins

2018

Applied Soft Computing

220

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“…The first is a study conducted by Fu et al [11], they suggest to build a C 4.5 decision tree for a set of realistic marketing data using a genetic algorithm. The data for this study is divided into a training, recording and testing groups.…”

Section: Literature Reviewmentioning

confidence: 99%

Utilizing the Genetic Algorithm to Pruning the C4.5 Decision Tree Algorithm

Mijwil

Abttan

2021

AJAS

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A decision tree (DTs) is one of the most popular machine learning algorithms that divide data repeatedly to form groups or classes. It is a supervised learning algorithm that can be used on discrete or continuous data for classification or regression. The most traditional classifier in this algorithm is the C4.5 decision tree, which is the point of this research. This classifier has the advantage of building a vast data set and does not stop until it reaches the desired goal. The problem with this classifier is that there are unnecessary nodes and branches leading to overfitting. This overfitting can negatively affect the classification process. In this context, the authors suggest utilizing a genetic algorithm to prune the effect of overfitting. This dataset study consists of four datasets: IRIS, Car Evaluation, GLASS, and WINE collected from UC Irvine (UCI) machine learning repository. The experimental results have confirmed the effectiveness of the genetic algorithm in pruning the effect of overfitting on the four datasets and optimizing confidence factor (CF) of the C4.5 decision tree. The proposed method has reached about 92% accuracy in this work.

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“…The presented model was evaluated and an accuracy of 87% was produced. A multi-combination of Attribute selection, clustering, Genetic algorithm and decision trees was developed and implemented by Reference [ 19 ] for diabetes risk prediction. It combined and built the optimal decision trees on the basis of predefined threshold criteria.…”

Section: Literature Reviewmentioning

confidence: 99%

EAGA-MLP—An Enhanced and Adaptive Hybrid Classification Model for Diabetes Diagnosis

Mishra

Tripathy

Mallick

et al. 2020

Sensors

126

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Disease diagnosis is a critical task which needs to be done with extreme precision. In recent times, medical data mining is gaining popularity in complex healthcare problems based disease datasets. Unstructured healthcare data constitutes irrelevant information which can affect the prediction ability of classifiers. Therefore, an effective attribute optimization technique must be used to eliminate the less relevant data and optimize the dataset for enhanced accuracy. Type 2 Diabetes, also called Pima Indian Diabetes, affects millions of people around the world. Optimization techniques can be applied to generate a reliable dataset constituting of symptoms that can be useful for more accurate diagnosis of diabetes. This study presents the implementation of a new hybrid attribute optimization algorithm called Enhanced and Adaptive Genetic Algorithm (EAGA) to get an optimized symptoms dataset. Based on readings of symptoms in the optimized dataset obtained, a possible occurrence of diabetes is forecasted. EAGA model is further used with Multilayer Perceptron (MLP) to determine the presence or absence of type 2 diabetes in patients based on the symptoms detected. The proposed classification approach was named as Enhanced and Adaptive-Genetic Algorithm-Multilayer Perceptron (EAGA-MLP). It is also implemented on seven different disease datasets to assess its impact and effectiveness. Performance of the proposed model was validated against some vital performance metrics. The results show a maximum accuracy rate of 97.76% and 1.12 s of execution time. Furthermore, the proposed model presents an F-Score value of 86.8% and a precision of 80.2%. The method is compared with many existing studies and it was observed that the classification accuracy of the proposed Enhanced and Adaptive-Genetic Algorithm-Multilayer Perceptron (EAGA-MLP) model clearly outperformed all other previous classification models. Its performance was also tested with seven other disease datasets. The mean accuracy, precision, recall and f-score obtained was 94.7%, 91%, 89.8% and 90.4%, respectively. Thus, the proposed model can assist medical experts in accurately determining risk factors of type 2 diabetes and thereby help in accurately classifying the presence of type 2 diabetes in patients. Consequently, it can be used to support healthcare experts in the diagnosis of patients affected by diabetes.

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The Use of Genetic Algorithm, Clustering and Feature Selection Techniques in Construction of Decision Tree Models for Credit Scoring

Abstract: ABSTRACT

Cited by 10 publications

References 59 publications

Information gain directed genetic algorithm wrapper feature selection for credit rating

Information gain directed genetic algorithm wrapper feature selection for credit rating

Utilizing the Genetic Algorithm to Pruning the C4.5 Decision Tree Algorithm

EAGA-MLP—An Enhanced and Adaptive Hybrid Classification Model for Diabetes Diagnosis

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