Swarm intelligence for data mining classification tasks: an experimental study using medical decision problems

Abstract

In the data-mining field, classifier learning techniques focus on extracting the knowledge from a problem using a set of labeled objects in order to predict the class for any other new object. Even though there exist several approaches to build these classifiers, some learning paradigms guide the creation of classifiers through an optimization process of a measure computed over the data. In this context, function optimization methods based on swarm intelligence can make the most of the interactions between individuals and be employed as a tool to explore a variety of potential solutions until reaching the convergence to a final and accurate solution for the classification problem. The present chapter, following the philosophy of this book, is aimed at guiding the reader through the whole process, from concepts to applications, when we refer to swarm intelligence applied to classification tasks. Thus, firstly, it presents a detailed explanation on how swarm intelligence algorithms can be used in classifier-building processes. In order to accomplish this, two of the most well-known classification techniques within this group of methods, which belong to particle swarm and ant colony optimizations, are analyzed. Second, the application of such methods for medical data classification is studied considering several real-world datasets. The results obtained show that swarm intelligence methods can play an important role for data analysis in medical applications, providing good performance results and models characterized by a high interpretability.