Panagiotis Pintelas

A cost sensitive technique for ordinal classification problems

A class of problems between classification and regression, learning to predict ordinal classes, has not received much attention so far, even though there are many problems in the real world that fall into that category. Given ordered classes, one is not only interested in maximizing the classification accuracy, but also in minimizing the distances between the actual and the predicted classes. This paper provides a systematic study on the various methodologies that have tried to handle this problem and presents an experimental study of these methodologies with a cost sensitive technique that uses fixed and unequal misclassification costs between classes. It concludes that this technique can be a more robust solution to the problem because it minimizes the distances between the actual and the predicted classes, without harming but actually slightly improving the classification accuracy.

A Descent Dai-Liao Conjugate Gradient Method Based on a Modified Secant Equation and Its Global Convergence

We propose a conjugate gradient method which is based on the study of the Dai-Liao conjugate gradient method. An important property of our proposed method is that it ensures sufficient descent independent of the accuracy of the line search. Moreover, it achieves a high-order accuracy in approximating the second-order curvature information of the objective function by utilizing the modified secant condition proposed by Babaie-Kafaki et al. (2010). Under mild conditions, we establish that the proposed method is globally convergent for general functions provided that the line search satisfies the Wolfe conditions. Numerical experiments are also presented.

DSS-PSP - A Decision Support Software for Evaluating Students’ Performance

Prediction, utilizing machine learning and data mining techniques is a significant tool, offering a first step and a helping hand for educators to early recognize those students who are likely to exhibit poor performance. In this work, we introduce a new decision support software for predicting the students’ performance at the final examinations. The proposed software is based on a novel 2-level classification technique which achieves better performance than any examined single learning algorithm. Furthermore, significant advantages of the presented tool are its simple and user-friendly interface and that it can be deployed in any platform under any operating system.

A limited memory descent Perry conjugate gradient method

In this work, we present a new limited memory conjugate gradient method which is based on the study of Perry’s method. An attractive property of the proposed method is that it corrects the loss of orthogonality that can occur in ill-conditioned optimization problems, which can decelerate the convergence of the method. Moreover, an additional advantage is that the memory is only used to monitor the orthogonality relatively cheaply; and when orthogonality is lost, the memory is used to generate a new orthogonal search direction. Under mild conditions, we establish the global convergence of the proposed method provided that the line search satisfies the Wolfe conditions. Our numerical experiments indicate the efficiency and robustness of the proposed method.

A modified Perry conjugate gradient method and its global convergence

In this work, we propose a new conjugate gradient method which consists of a modification of Perry’s method and ensures sufficient descent independent of the accuracy of the line search. An important property of our proposed method is that it achieves a high-order accuracy in approximating the second order curvature information of the objective function by utilizing a new modified secant condition. Moreover, we establish that the proposed method is globally convergent for general functions provided that the line search satisfies the Wolfe conditions. Our numerical experiments indicate that our proposed method is preferable and in general superior to classical conjugate gradient methods in terms of efficiency and robustness.

A new class of nonmonotone conjugate gradient training algorithms

In this paper, we propose a new class of conjugate gradient algorithms for training neural networks which is based on a new modified nonmonotone scheme proposed by Shi and Wang (2011). The utilization of a nonmonotone strategy enables the training algorithm to overcome the case where the sequence of iterates runs into the bottom of a curved narrow valley, a common occurrence in neural network training process. Our proposed class of methods ensures sufficient descent, avoiding thereby the usual inefficient restarts and it is globally convergent under mild conditions. Our experimental results provide evidence that the proposed nonmonotone conjugate gradient training methods are efficient, outperforming classical methods, proving more stable, efficient and reliable learning.

A descent hybrid conjugate gradient method based on the memoryless BFGS update

In this work, we present a new hybrid conjugate gradient method based on the approach of the convex hybridization of the conjugate gradient update parameters of DY and HS+, adapting a quasi-Newton philosophy. The computation of the hybrization parameter is obtained by minimizing the distance between the hybrid conjugate gradient direction and the self-scaling memoryless BFGS direction. Furthermore, a significant property of our proposed method is that it ensures sufficient descent independent of the accuracy of the line search. The global convergence of the proposed method is established provided that the line search satisfies the Wolfe conditions. Our numerical experiments on a set of unconstrained optimization test problems from the CUTEr collection indicate that our proposed method is preferable and in general superior to classic conjugate gradient methods in terms of efficiency and robustness.

An Ensemble-Based Semi-Supervised Approach for Predicting Students’ Performance

Educational data mining has gained popularity due to its ability to provide useful knowledge hidden in data of students’ records for better educational decision-making support. During the last years, a variety of methods have been applied to develop accurate models to monitor students’ behavior and performance, while most of these studies examine the efficiency of supervised classification methods. In this work, we propose a new ensemble-based semi-supervised method for the prognosis of students’ performance in the final examinations at the end of academic year. Our experimental results reveal that our proposed method is proved to be effective and practical for early student progress prediction as compared to some existing semi-supervised learning methods.

Improving the evaluation process of students’ performance utilizing a decision support software

During the last decades, educational data mining constitutes a significant tool, offering a first step and a helping hand in the prediction of students’ progress and performance. In this work, we present a user-friendly decision support software, for accurately predicting the students’ performance at the final examinations of the academic year. The proposed software incorporates a classification scheme which has two major features. Firstly, it identifies with high accuracy the students at-risk of failing the final examinations; secondly, it classifies the students based on their predicted grades. Our numerical experiments show that it achieves better performance than any examined single learning algorithm. The proposed software was developed to provide assistance to students’ evaluation and mostly to the early identification of students’ at-risk in order to take proper actions for improving their performance.

On Ensemble SSL Algorithms for Credit Scoring Problem

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.