Comparación de algoritmos evolutivos para la optimización en la clasificación de la obesidad en escolares

The aim of the study is to compare three different evolutionary algorithms: Real Encoding Particle Swarm Optimization (REPSO-C), Incremental Learning with Genetic Algorithms (ILGA) and Decision Tree with Genetic Algorithm (DT-GA) to determine the percent improvement during each iteration of each of the algorithms on a database related to school obesity. For this purpose, the Keel tool was used, which allowed data loading, preparation, processing and analysis of results, both in training and in the tests performed. The precision found by the execution of each of the algorithms, allows to conclude that the model using decision tree and genetic algorithm DT-GA is the best one due to the level of precision that it possesses.El estudio tiene por objetivo comparar tres tipos de algoritmos evolutivos diferentes: Real Encoding Particle Swarm Optimization (REPSO–C), Incremental Learning with Genetic Algorithms (ILGA) y Decision Tree with Genetic Algorithm (DT-GA), para determinar el porcentaje de mejora durante cada iteración que tiene cada uno de los algoritmos sobre una base de datos relacionadas con la obesidad escolar. Se utilizó la herramienta denominada Keel, que permitió cargar los datos, prepararlos, procesarlos y analizar los resultados, tanto en el entrenamiento, como en las pruebas realizadas. La precisión encontrada por la ejecución de cada uno de los algoritmos, permite concluir que el modelo que utiliza árbol de decisión y algoritmo genético DT-GA es el mejor, debido al nivel de precisión que posee

Classification Rule Discovery with Particle Swarm Optimization

[[abstract]]粒子群最佳化演算法(Particle Swarm Optimization; PSO)為人工智慧領域中的一項新興技術，由1995年發展至今，已經逐漸被廣泛地應用於最佳化問題與資料探勘領域。本研究提出以離散型的PSO演算法(Discrete PSO; DPSO)結合匹茲堡學說(Pittsburgh Approach)來建構分類系統，並且利用本研究所提出之法則遮罩(Rule Mask)的概念結合法則刪減運算子(Rule Deletion Operator)、最小敘述長度為基礎的適應函數(Minimum Description Length-based Fitness Function)兩種方法使粒子具有可變動長度的特性。 本研究也利用USD(Unparametrized Supervised Discretization)離散化演算法來進行連續型屬性的離散化(Discretization)，使所建構之分類系統能夠同時處理離散型與連續型屬性資料而不需要事先進行離散化的前置處理。研究中分別利用四個由UCI(UCI Machine Learning Repository)所取得之資料集來進行實驗測試，並且將結果和J48以及Entropy演算法所建構之分類系統進行分類效果之比較。 實驗結果顯示，本研究所提出之方法可以使粒子具有可變動長度的特性，分類系統也能夠同時處理不同型態的屬性資料。在分類準確率的比較方面，本研究所提出之分類系統具有J48演算法的分類水準，並且僅以較少的法則數目即可達到更高的分類效果，顯示出本研究所設計之分類系統具有競爭性。[[abstract]]Particle Swarm Optimization (PSO) is a new optimization technique in the artificial intelligence field. Since 1995, it has been gradually applied to the field of optimization and data mining. In this paper, we applied the discrete PSO with the Pittsburgh approach to build a PSO-based classifier. We also propose the concept of the rule mask and combine the rule deletion operator and the minimum description length-based fitness function to make the particle’s length variably. In this paper, we also apply the USD algorithm to make the classifier deal with the discrete and continuous attributes simultaneously. Four datasets obtained from the UCI were tested in our experiment, and the results are compared with that of the J48 and Entropy algorithms. The experimental results show that the classification performence of our method excels that of the J48. The proposed method also has the merits of variable number of rules and can simutaniously deal with continuous and discrete attributes