https://sci2s.ugr.es/sites/default/files/files/TematicWebSites/sicidm/2010-Garcia-INS.pdf

Advanced nonparametric tests for multiple comparisons in the design of experiments in computational intelligence and data mining: Experimental analysis of power

This paper introduces a software tool named KEEL which is a software tool to assess evolutionary algorithms for Data Mining problems of various kinds including as regression, classification, unsupervised learning, etc. It includes evolutionary learning algorithms based on different approaches: Pittsburgh, Michigan and IRL, as well as the integration of evolutionary learning techniques with different pre-processing techniques, allowing it to perform a complete analysis of any learning model in comparison to existing software tools. Moreover, KEEL has been designed with a double goal: research and educational.

/pdf/keel-a-software-tool-to-assess-evolutionary-algorithms-for-315o2llhde.pdf

KEEL: a software tool to assess evolutionary algorithms for data mining problems

(1997). Multiple Comparisons: Theory and Methods. Journal of Quality Technology: Vol. 29, No. 3, pp. 359-359.

Multiple Comparisons: Theory and Methods

In this paper we compare different data mining methods and techniques for classifying students based on their Moodle usage data and the final marks obtained in their respective courses. We have developed a specific mining tool for making the configuration and execution of data mining techniques easier for instructors. We have used real data from seven Moodle courses with Cordoba University students. We have also applied discretization and rebalance preprocessing techniques on the original numerical data in order to verify if better classifier models are obtained. Finally, we claim that a classifier model appropriate for educational use has to be both accurate and comprehensible for instructors in order to be of use for decision making.

https://www.educationaldatamining.org/EDM2008/uploads/proc/1_Romero_3.pdf

Data Mining Algorithms to Classify Students

This paper shows how web usage mining can be applied in e-learning systems in order to predict the marks that university students will obtain in the final exam of a course. We have also developed a specific Moodle mining tool oriented for the use of not only experts in data mining but also of newcomers like instructors and courseware authors. The performance of different data mining techniques for classifying students are compared, starting with the student's usage data in several Cordoba University Moodle courses in engineering. Several well-known classification methods have been used, such as statistical methods, decision trees, rule and fuzzy rule induction methods, and neural networks. We have carried out several experiments using all available and filtered data to try to obtain more accuracy. Discretization and rebalance pre-processing techniques have also been used on the original numerical data to test again if better classifier models can be obtained. Finally, we show examples of some of the models discovered and explain that a classifier model appropriate for an educational environment has to be both accurate and comprehensible in order for instructors and course administrators to be able to use it for decision making. © 2010 Wiley Periodicals, Inc. Comput Appl Eng Educ 21: 135–146, 2013

/pdf/web-usage-mining-for-predicting-final-marks-of-students-that-3dicyadj8x.pdf

Web usage mining for predicting final marks of students that use Moodle courses

https://sci2s.ugr.es/keel/pdf/algorithm/articulo/2006-NN-Evolutionary%20product%20unit%20based%20NN.pdf

Evolutionary product unit based neural networks for regression

This paper presents a hybrid evolutionary algorithm (EA) to solve nonlinear-regression problems. Although EAs have proven their ability to explore large search spaces, they are comparatively inefficient in fine tuning the solution. This drawback is usually avoided by means of local optimization algorithms that are applied to the individuals of the population. The algorithms that use local optimization procedures are usually called hybrid algorithms. On the other hand, it is well known that the clustering process enables the creation of groups (clusters) with mutually close points that hopefully correspond to relevant regions of attraction. Local-search procedures can then be started once in every such region. This paper proposes the combination of an EA, a clustering process, and a local-search procedure to the evolutionary design of product-units neural networks. In the methodology presented, only a few individuals are subject to local optimization. Moreover, the local optimization algorithm is only applied at specific stages of the evolutionary process. Our results show a favorable performance when the regression method proposed is compared to other standard methods.

/pdf/hybridization-of-evolutionary-algorithms-and-local-search-by-skiy9ugu3b.pdf

Hybridization of evolutionary algorithms and local search by means of a clustering method

Evolutionary product-unit neural networks classifiers

Massive missing data reconstruction in ocean buoys with evolutionary product unit neural networks

We propose a classification method based on a special class of feed-forward neural network, namely product-unit neural networks. They are based on multiplicative nodes instead of additive ones, where the nonlinear basis functions express the possible strong interactions between variables. We apply an evolutionary algorithm to determine the basic structure of the product-unit model and to estimate the coefficients of the model. We use softmax transformation as the decision rule and the cross-entropy error function because of its probabilistic interpretation. The empirical results over four benchmark data sets show that the proposed model is very promising in terms of classification accuracy and the complexity of the classifier, yielding a state-of-the-art performance.

/pdf/evolutionary-product-unit-neural-networks-for-classification-3kui87aa7u.pdf

A. C. Martínez-Estudillo

Papers

Evolutionary product unit based neural networks for regression

Hybridization of evolutionary algorithms and local search by means of a clustering method

Evolutionary product-unit neural networks classifiers

Massive missing data reconstruction in ocean buoys with evolutionary product unit neural networks

Evolutionary product-unit neural networks for classification