Data Mining Practical Machine Learning Tools and Techniques

This paper presents an overview of our most recent results concerning the Particle Swarm Optimization (PSO) method. Techniques for the alleviation of local minima, and for detecting multiple minimizers are described. Moreover, results on the ability of the PSO in tackling Multiobjective, Minimax, Integer Programming and e1 errors-in-variables problems, as well as problems in noisy and continuously changing environments, are reported. Finally, a Composite PSO, in which the heuristic parameters of PSO are controlled by a Differential Evolution algorithm during the optimization, is described, and results for many well-known and widely used test functions are given.

Recent approaches to global optimization problems through Particle Swarm Optimization

H E Stanley Oxford: University Press 1971 pp xx + 308 price ?5 In the past fifteen years or so there has been a lot of experimental and theoretical work on the nature of critical phenomena in the neighbourhood of second order phase transitions. It has not been easy to get a good overall view of this work without digging through the rather complex original literature, although there are some good review articles covering particular aspects of the work.

https://iopscience.iop.org/article/10.1088/0031-9112/23/4/018/pdf

Introduction to Phase Transitions and Critical Phenomena

Nonlinear eigenvalue problems arise in a variety of science and engineering applications and in the past ten years there have been numerous breakthroughs in the development of numerical methods. This article surveys nonlinear eigenvalue problems associated with matrix-valued functions which depend nonlinearly on a single scalar parameter, with a particular emphasis on their mathematical properties and available numerical solution techniques. Solvers based on Newton's method, contour integration, and sampling via rational interpolation are reviewed. Problems of selecting the appropriate parameters for each of the solver classes are discussed and illustrated with numerical examples. This survey also contains numerous MATLAB code snippets that can be used for interactive exploration of the discussed methods.

/pdf/the-nonlinear-eigenvalue-problem-316eav07xe.pdf

The nonlinear eigenvalue problem

Numerical Methods for Unconstrained Optimization

Semi-supervised regression: A recent review

Transferring knowledge from one domain to another has gained a lot of attention among scientists in recent years. Transfer learning is a machine learning approach aiming to exploit the knowledge retrieved from one problem for improving the predictive performance of a learning model for a different but related problem. This is particularly the case when there is a lack of data regarding a problem, but there is plenty of data about another related one. To this end, the present study intends to investigate the effectiveness of transfer learning from deep neural networks for the task of students’ performance prediction in higher education. Since building predictive models in the Educational Data Mining field through transfer learning methods has been poorly studied so far, we consider this study as an important step in this direction. Therefore, a plethora of experiments were conducted based on data originating from five compulsory courses of two undergraduate programs. The experimental results demonstrate that the prognosis of students at risk of failure can be achieved with satisfactory accuracy in most cases, provided that datasets of students who have attended other related courses are available.

Transfer Learning from Deep Neural Networks for Predicting Student Performance

Educational Data Mining (EDM) has emerged over the last two decades, concerning with the development and implementation of data mining methods in order to facilitate the analysis of vast amounts of data originating from a wide variety of educational contexts. Predicting students’ progression and learning outcomes, such as dropout, performance and course grades, is regarded among the most important tasks of the EDM field. Therefore, applying appropriate machine learning algorithms for building accurate predictive models is of outmost importance for both educators and data scientists. Considering the high-dimensional input space and the complexity of machine learning algorithms, the process of building accurate and robust learning models requires advanced data science skills, while is time-consuming and error-prone in most cases. In addition, choosing the proper method for a given problem formulation and configuring the optimal parameters’ values for a specific model is a demanding task, whilst it is often very difficult to understand and explain the produced results. In this context, the main purpose of the present study is to examine the potential use of advanced machine learning strategies on educational settings from the perspective of hyperparameter optimization. More specifically, we investigate the effectiveness of automated Machine Learning (autoML) for the task of predicting students’ learning outcomes based on their participation in online learning platforms. At the same time, we limit the search space to tree-based and rule-based models in order to achieving transparent and interpretable results. To this end, a plethora of experiments were carried out, revealing that autoML tools achieve consistently superior results. Hopefully our work will help nonexpert users (e.g., educators and instructors) in the field of EDM to conduct experiments with appropriate automated parameter configurations, thus achieving highly accurate and comprehensible results.

Implementing AutoML in Educational Data Mining for Prediction Tasks

The regions of stability for the ‘out of plane’ equilibrium points of the photogravitational restricted three-body problem are given. Second order expansions of periodic solutions around these points are constructed and the corresponding families are computed. It is found that two such families exist. One of them originates and terminates on the same equilibrium point while the other terminates by flattening on the orbital plane.

Periodic solutions about the out of plane equilibrium points in the photogravitational restricted three-body problem

https://www.math.upatras.gr/~vrahatis/papers/journals/KravanjaVRVZ00_COMPUT_PHYS_COMMUN_124_pp212-232_2000.pdf

Omiros Ragos

Papers

Semi-supervised regression: A recent review

Transfer Learning from Deep Neural Networks for Predicting Student Performance

Implementing AutoML in Educational Data Mining for Prediction Tasks

Periodic solutions about the out of plane equilibrium points in the photogravitational restricted three-body problem

ZEAL: A mathematical software package for computing zeros of analytic functions