دانلود مقاله ISI انگلیسی شماره 48294
عنوان فارسی مقاله

مدل پیش بینی ورشکستگی جدید بر اساس روش نزدیکترین K مجاور فازی تطبیقی

کد مقاله سال انتشار مقاله انگلیسی ترجمه فارسی تعداد کلمات
48294 2011 12 صفحه PDF سفارش دهید محاسبه نشده
خرید مقاله
پس از پرداخت، فوراً می توانید مقاله را دانلود فرمایید.
عنوان انگلیسی
A novel bankruptcy prediction model based on an adaptive fuzzy k-nearest neighbor method
منبع

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

Journal : Knowledge-Based Systems, Volume 24, Issue 8, December 2011, Pages 1348–1359

کلمات کلیدی
نزدیکترین K مجاور فازی ؛ محاسبات موازی - بهینه سازی اجتماع ذرات ؛ انتخاب ویژگی؛ پیش بینی ورشکستگی
پیش نمایش مقاله
پیش نمایش مقاله مدل پیش بینی ورشکستگی جدید بر اساس روش نزدیکترین K مجاور فازی تطبیقی

چکیده انگلیسی

Bankruptcy prediction is one of the most important issues in financial decision-making. Constructing effective corporate bankruptcy prediction models in time is essential to make companies or banks prevent bankruptcy. This study proposes a novel bankruptcy prediction model based on an adaptive fuzzy k-nearest neighbor (FKNN) method, where the neighborhood size k and the fuzzy strength parameter m are adaptively specified by the continuous particle swarm optimization (PSO) approach. In addition to performing the parameter optimization for FKNN, PSO is also utilized to choose the most discriminative subset of features for prediction. Adaptive control parameters including time-varying acceleration coefficients (TVAC) and time-varying inertia weight (TVIW) are employed to efficiently control the local and global search ability of PSO algorithm. Moreover, both the continuous and binary PSO are implemented in parallel on a multi-core platform. The proposed bankruptcy prediction model, named PTVPSO-FKNN, is compared with five other state-of-the-art classifiers on two real-life cases. The obtained results clearly confirm the superiority of the proposed model in terms of classification accuracy, Type I error, Type II error and area under the receiver operating characteristic curve (AUC) criterion. The proposed model also demonstrates its ability to identify the most discriminative financial ratios. Additionally, the proposed model has reduced a large amount of computational time owing to its parallel implementation. Promisingly, PTVPSO-FKNN might serve as a new candidate of powerful early warning systems for bankruptcy prediction with excellent performance.

خرید مقاله
پس از پرداخت، فوراً می توانید مقاله را دانلود فرمایید.