An improved particle swarm optimization with EA mutation for data classification
Liu Qiu-lian · 2009
Data classification is an important data mining task. Various optimization techniques have been proposed to improve the performance of data classification. In this paper we propose a novel algorithm for data classification that we call particle swarm optimization with EA mutation. To evaluate its usefulness, we empirically compare the performance of our algorithm with another evolutionary algorithm, namely a Genetic Algorithm, in rule discovery for classification tasks. Such tasks are considered core tools for Decision Support Systems in a widespread urea, ranging from the industry, commerce, military and scientific fields. The data sources used here for experimental testing are commonly used and considered us a standard for rule discovery algorithms reliability ranking. The results obtained in these domains seem to indicate that our algorithm is competitive with other evolutionary techniques.