A Clustering Based Classifier Ensemble Approach to Corporate Bankruptcy Prediction

Year 2018, Volume 6, Issue 2, 2018, 365 - 376, 31.12.2018

Aytuğ Onan

https://doi.org/10.17093/alphanumeric.333785

Cited By: 2

Abstract

Corporate bankruptcy prediction is an important research direction in finance. Building a robust prediction scheme for bankruptcy can be beneficial to several stakeholders, including management organizations, government and stockholders. Ensemble learning is a well-known technique to improve the predictive performance of classification algorithms by decreasing the generalization error and enhancing the classification accuracy. It has been a well-established technique in bankruptcy prediction to enhance the predictive performance. Diversity plays an essential role in constructing robust ensemble classification schemes. In this paper, a clustering based classifier ensemble approach is presented for corporate bankruptcy prediction. In this scheme, k-means algorithm is utilized to obtain diversified training subsets. Based on the subsets, each base learning algorithms are trained and the predictions of base learning algorithms are combined by a majority voting scheme. In the empirical analysis, four classification algorithms (namely, C4.5 algorithm, k-nearest neighbour algorithm, support vector machines and logistic regression) and three ensemble learning methods (Bagging, AdaBoost and Random Subspace) are evaluated.

Keywords

Corporate Bankruptcy Prediction, Ensemble Learning, Clustering

Firma Başarısızlığının Tahmin Edilmesi İçin Kümelemeye Dayalı Bir Sınıflandırıcı Topluluğu Yaklaşımı

Abstract

Firma başarısızlıklarının tahmin edilmesi, finansta önemli bir araştırma yönüdür. Güvenilir başarısızlık tahmin etme modellerinin geliştirilmesi, aralarında yönetim organizasyonlarının, devlet kurumlarının ve hisse senedi sahiplerinin de yer aldığı birçok farklı paydaş için oldukça yararlı olabilmektedir. Topluluk öğrenmesi yöntemi, genelleştirme hatasını azaltarak ve doğru sınıflandırma oranını artırarak, sınıflandırma algoritmalarının tahmin etme başarımını artıran önemli bir tekniktir. Topluluk öğrenmesi, firma başarısızlıklarının tahmin edilmesinde kullanılan yaygın kullanıma sahip bir yöntemdir. Yüksek başarımlı sınıflandırıcı topluluklarının oluşturulmasında çeşitlilik önemli bir rol oynamaktadır. Bu çalışmada, firma başarısızlıkların tahmin edilmesi için kümelemeye dayalı bir sınıflandırıcı topluluğu yaklaşımı sunulmaktadır. Önerilen tasarıda, k-ortalama algoritması kullanılarak, çeşitlendirilmiş eğitim alt kümeleri oluşturulmaktadır. Bu eğitim alt kümelerine dayalı olarak, sınıflandırıcı topluluğunda yer alan her bir temel öğrenme algoritması eğitilmekte ve temel öğrenme yöntemlerinin bireysel çıktıları çoğunluk oylaması aracılığıyla birleştirilmektedir. Deneysel analizlerde, dört sınıflandırma algoritması (C4.5 algoritması, k-en yakın komşu algoritması, destek vektör makineleri ve lojistik regresyon) ve üç topluluk öğrenmesi yöntemi (Bagging, AdaBoost ve rastgele alt uzay) değerlendirilmiştir.

Keywords

Firma Başarısızlığının Tahmin Edilmesi, Topluluk Öğrenmesi, Kümeleme

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