دانلود مقاله ISI انگلیسی شماره 46039
عنوان فارسی مقاله

یک مدل ترکیبی داده کاوی از الگوریتم های انتخاب ویژگی و طبقه بندی کننده یادگیری گروه برای امتیاز دهی اعتباری

کد مقاله سال انتشار مقاله انگلیسی ترجمه فارسی تعداد کلمات
46039 2015 13 صفحه PDF سفارش دهید 9710 کلمه
خرید مقاله
پس از پرداخت، فوراً می توانید مقاله را دانلود فرمایید.
عنوان انگلیسی
A hybrid data mining model of feature selection algorithms and ensemble learning classifiers for credit scoring
منبع

Publisher : Elsevier - Science Direct (الزویر - ساینس دایرکت)

Journal : Journal of Retailing and Consumer Services, Volume 27, November 2015, Pages 11–23

کلمات کلیدی
اعتبارسنجی - تقسیم بندی - انتخاب ویژگی - آموزش گروه - داده کاوی
پیش نمایش مقاله
پیش نمایش مقاله یک مدل ترکیبی داده کاوی از الگوریتم های انتخاب ویژگی و طبقه بندی کننده یادگیری گروه برای امتیاز دهی اعتباری

چکیده انگلیسی

Data mining techniques have numerous applications in credit scoring of customers in the banking field. One of the most popular data mining techniques is the classification method. Previous researches have demonstrated that using the feature selection (FS) algorithms and ensemble classifiers can improve the banks' performance in credit scoring problems. In this domain, the main issue is the simultaneous and the hybrid utilization of several FS and ensemble learning classification algorithms with respect to their parameters setting, in order to achieve a higher performance in the proposed model. As a result, the present paper has developed a hybrid data mining model of feature selection and ensemble learning classification algorithms on the basis of three stages. The first stage, as expected, deals with the data gathering and pre-processing. In the second stage, four FS algorithms are employed, including principal component analysis (PCA), genetic algorithm (GA), information gain ratio, and relief attribute evaluation function. In here, parameters setting of FS methods is based on the classification accuracy resulted from the implementation of the support vector machine (SVM) classification algorithm. After choosing the appropriate model for each selected feature, they are applied to the base and ensemble classification algorithms. In this stage, the best FS algorithm with its parameters setting is indicated for the modeling stage of the proposed model. In the third stage, the classification algorithms are employed for the dataset prepared from each FS algorithm. The results exhibited that in the second stage, PCA algorithm is the best FS algorithm. In the third stage, the classification results showed that the artificial neural network (ANN) adaptive boosting (AdaBoost) method has higher classification accuracy. Ultimately, the paper verified and proposed the hybrid model as an operative and strong model for performing credit scoring.

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