On Ensemble SSL Algorithms for Credit Scoring Problem

Abstract

Credit scoring is generally recognized as one of the most significant operational research techniques used in banking and finance, aiming to identify whether a credit consumer belongs to either a legitimate or a suspicious customer group. With the vigorous development of the Internet and the widespread adoption of electronic records, banks and financial institutions have accumulated large repositories of labeled and mostly unlabeled data. Semi-supervised learning constitutes an appropriate machine- learning methodology for extracting useful knowledge from both labeled and unlabeled data. In this work, we evaluate the performance of two ensemble semi-supervised learning algorithms for the credit scoring problem. Our numerical experiments indicate that the proposed algorithms outperform their component semi-supervised learning algorithms, illustrating that reliable and robust prediction models could be developed by the adaptation of ensemble techniques in the semi-supervised learning framework.

A Weighted Voting Ensemble Self-Labeled Algorithm for the Detection of Lung Abnormalities from X-Rays

“…Furthermore, the base learners utilized in all self-labeled algorithms are the Sequential Minimum Optimization (SMO) [33], the C4.5 decision tree algorithm [34] and the kNN algorithm [35] as in [2,[7][8][9], which probably constitute the most effective and popular machine learning algorithms for classification problems [36].…”

“…The statistical comparison of several classification algorithms over multiple datasets is fundamental in the area of machine learning and it is usually performed by means of a statistical test [2,[7][8][9]. Since our motivation stems from the fact that we are interested in evaluating the rejection of the hypothesis that all the algorithms perform equally well for a given level based on their classification accuracy and highlighting the existence of significant differences between our proposed algorithm and the classical self-labeled algorithms, we utilized the non-parametric Friedman Aligned Ranking (FAR) [37] test.…”

“…These algorithms efficiently develop powerful classifiers by meaningfully relating the explicit classification information of labeled data with the information hidden in the unlabeled data [3,4]. Self-labeled algorithms probably constitute the most popular class of SSL algorithms due to their simplicity of implementation, their wrapper-based philosophy and good classification performance [2,[5][6][7][8]. This class of algorithms exploits a large amount of unlabeled data via a self-learning process based on supervised learners.…”

“…Each bootstrap sample contains about 2/3 of the original training set, where each example can appear multiple times. Recently, a new approach has been given by Livieris et al [2,8,16,17] and Livieris [18] in which some ensemble self-labeled algorithms are proposed based on voting schemes. The proposed algorithms exploit the individual predictions of the most efficient and frequently used self-labeled algorithms using simple voting methodologies.Motivated by these works, we propose a new semi-supervised self-labeled algorithm which is based on a sophisticated ensemble philosophy.…”

Forecasting Economy-Related Data Utilizing Weight-Constrained Recurrent Neural Networks

“…Livieris et al [17] evaluated the performance of two ensemble semi-supervised learning algorithms for the credit scoring problem. The proposed algorithms exploit the predictions of three of the most efficient and popular self-labeled algorithms: self-training, co-training, and tri-training, using different voting methodologies.…”