Celso Yoshikazu Ishida

Multiobjective Optimization and Rule Learning: Subselection Algorithm or Meta-heuristic Algorithm?

A previous work explores a Multi-Objective Subset Selection algorithm, denominated the Pareto Front Elite, to induce classifiers. These classifiers are composed by a set of rules selected following Pareto dominance concepts and forming unordered classifiers. These rules are previously created by an association rule algorithm. The performance of the classifiers induced were compared with other well known rule induction algorithms using the area under the ROC curve. The area under the ROC curve (AUC) is considered a relevant criterion to deal with imbalanced data, misclassification costs and noisy data. The results show that the Pareto Front Elite algorithm is comparable to the best known techniques. In this paper we explore multi-objective meta-heuristic approach to create rules and to build the Pareto Front using the sensitivity and specificity criteria, the chosen Metaheuristic is a Greedy Randomized Adaptive Search Procedure (GRASP) with path-relinking. We perform an experimental study to compare the two algorithms: one based on a complete set of rules, and the other based on Metaheuristic Approach. In this study we analyze the classification results, through the AUC criterion, and the Pareto Front coverage produced by each algorithm.

Exploring multi-objective PSO and GRASP-PR for rule induction

A process for restructuring meat wherein chunked and wafer sliced meats are blended to promote release of natural binding proteins. The blended meats are formed into log shapes for freezing and tempering, and then pressed into shape and sliced into steaks or chops. The process produces a roast if the final slicing step is eliminated.

GPSQL Miner: SQL-grammar genetic programming in data mining

The present work describes GPSQL Miner, a genetic programming system for mining relational databases. This system uses grammar genetic programming for classifications task and one of its main features is the representation of the classifiers. The system uses SQL grammar, which facilitates the evaluation process, once the data are in relational databases. The tool was tested with some databases and the results were compared with other algorithms. These first experiments have shown promising results for the classification task.

Optimization of the AUC Criterion for Rule Subset Selection

The area under the ROC curve (AUC) is considered a relevant criterion to deal with imbalanced data, misclassification costs and noisy data. Based on this preference, we present an algorithm for rule subset selection. The algorithm builds a Pareto Front using the Sensitivity and Specificity criteria selecting rules from a large set of rules. An empirical study is carried out to verify the influence of the A priori Parameter in Pareto Front Elite Algorithm. We compare our results with other rule induction algorithms and the results show that the new algorithm obtains a set of rules with greater values of the AUC.

Análise das Plataformas de Desenvolvimento Mobile aplicados na Área Educacional, usando Android e Windows Phone. Estudo de Caso: Aplicativo Planetas no Windows Phone

Esta pesquisa exibe uma analise comparativa de duas plataformas de desenvolvimento de tecnologias moveis: Android SDK para Eclipse IDE (Android) e o Windows Phone SDK para Visual Studio IDE (WP) e como a concepcao de aplicativos moveis podem ser utilizados na area da educacao. A metodologia e uma abordagem empirica atraves de comparativo das plataformas, os procedimentos sao bservacionais de acordo com a disponibilidade de funcoes para realizacao de um aplicativo e do nivel de dificuldade de execucao dos comandos de manipulacao, que o desenvolvedor de software pode encontrar. Os resultados desta pesquisa mostram que o Windows Phone apresenta o melhor desempenho no Ambiente de Desenvolvimento Integrado, sendo a ferramenta mais eficiente para criacao visual da interface do aplicativo.