Classifiers and rating scores are prone to implicitly codifying biases, which may be present in the training data, against protected classes (i.e., age, gender, or race). So it is important to understand how to design classifiers and scores that prevent discrimination in predictions. This paper develops computationally tractable algorithms for designing accurate but fair support vector machines...

We describe a method for solving large-scale semiparametric support vector machines (SVMs) for regression problems. Most of the approaches proposed to date for large-scale SVMs cannot accommodate the multiple equality constraints that appear in semiparametric problems. Our approach uses a decomposition framework, with a primal-dual algorithm to find an approximate saddle point for the min-max f...

Abstract In machine learning, hyperparameter tuning is strongly useful to improve model performance. our research, we concentrate attention on classifying imbalanced data by cost-sensitive support vector machines. We propose a multi-objective approach that optimizes model’s hyper-parameters. The devised for data. Three SVM performance measures are optimized. present the algorithm in basic versi...

Support Vector Machines SVMs have proven to be highly e ective for learning many real world datasets but have failed to establish them selves as common machine learning tools This is partly due to the fact that they are not easy to implement and their standard imple mentation requires the use of optimization packages In this paper we present simple iterative algorithms for training support vect...

The previous study of fuzzy support vector machines (FSVMs) provides a method to classify data with noises or outliers by manually associating each data point with a fuzzy membership that can reflect their relative degrees as meaningful data. In this paper, we introduce two factors in training data points, the confident factor and the trashy factor, and automatically generate fuzzy memberships ...

Although several papers have studied no-idle scheduling problems, they all focus on flow shops, assuming one processor at each working stage. But, companies commonly extend to hybrid flow shops by duplicating machines in parallel in stages. This paper considers the problem of scheduling no-idle hybrid flow shops. A mixed integer linear programming model is first developed to mathematically form...

Quantitative methods to assess the creditworthiness of the loan applicants are vital for the profitability and the transparency of the lending business. With the total loan volumes typical for traditional financial institutions, even the slightest improvement in credit scoring models can translate into substantial additional profit. Yet for the regulatory reasons and due to the potential model ...

The assessment of credit risk usually involves the development of rating models that classify credit applicants (firms or individuals) into predefined risk groups. A plethora of methodologies have been proposed to develop such rating models. Among them support vector machines (SVMs) have rapidly evolved in statistical learning theory as new modeling technique for developing classification model...

One of the most important research issues in finance is building accurate corporate bankruptcy prediction models since they are essential for the risk management of financial institutions. Thus, researchers have applied various data-driven approaches to enhance prediction performance including statistical and artificial intelligence techniques. Recently, support vector machines (SVMs) are becom...

The widely used Support Vector Machine (SVM) method has shown to yield very good results in Supervised Classification problems. Other methods such as Classification Trees have become more popular among practitioners than SVM thanks to their interpretability, which is an important issue in Data Mining. In this work, we propose an SVM-based method that automatically detects the most important pre...