Silhouette-based feature selection for classification of medical images

Classification is an important task for computer-aided diagnosis systems (CADs). However, many classifiers may not perform well, presenting poor generalization and high computational cost, especially when dealing with high-dimensional datasets. Thus, feature selection can greatly mitigate these problems. In this paper, we propose two filter-based feature selection algorithms that calculate the simplified silhouette statistic as evaluation function: the silhouette-based greedy search (SiGS) and the silhouette-based genetic algorithm search (SiGAS). Silhouette statistic is used to guide the search for features that provide better class separability. Experiments performed on three datasets have shown that the SiGAS algorithm overcomes traditional filter algorithms, such as CFS, FCBF and reliefF. It also outperforms a similar algorithm, kNNGAS, based on genetic algorithm that minimizes the classification error of k-nearest neighbors. Additionally, results have shown that SiGAS produces better accuracy than SiGS.