Biomedical Classification Problems Automatically Solved by Computational Intelligence Methods

Abstract

Biomedical classification problems are of great interest to both medical practitioners and computer scientists. Due to the harmful consequences of a wrong decision in this ambit, computational methods must be carefully designed to provide a reliable tool for helping physicians to obtain accurate predictions on unseen cases. Computational Intelligence (CI) provides robust models to perform optimization, classification and regression tasks. These models have been previously designed, mainly based on the expertise of computer scientists, to solve a vast number of biomedical problems. As the number of both CI algorithms and biomedical problems continues to grow, selecting the right method to solve a given problem becomes more challenging. To deal with this complexity, a systematic methodology for selecting a suitable model for a given classification problem is required. In this work, we review the more promising classification and optimization algorithms and reformulate them into a synergic framework to automatically design and optimize pattern classifiers. Our proposal, including state-of-the-art evolutionary algorithms and support vector machines, is tested on a variety of biomedical problems. Experimental results on benchmark datasets allow us to conclude that the automatically designed classifiers reach higher or equal performance than those designed by computer specialists.

HC-DTTSVM: A Network Intrusion Detection Method Based on Decision Tree Twin Support Vector Machine and Hierarchical Clustering

“…SVM and its variants are very important statistical machine learning algorithms. SVMs have the advantage of minimizing structural risk and having a relatively high generalization ability, and can efficiently solve many pattern classification problems [25]- [27]. By combining SVMs with other techniques, researchers have proposed many network intrusion detection methods.…”