Feature Selection Using ABC for the Lung CT Scan Images

Feature Selection is an important preprocessing step for most machine learning algorithms especially pattern classification. Feature Selection aims in determining the most relevant and useful subset of features from the dataset representing any application domain, without compromising the predictive accuracy represented by the actual set of features. There are many meta-heuristic search algorithms are used to solving combinatorial optimization problems. This paper aims at investigating, implementing, and analyzing a feature selection method using the Artificial Bee Colony approach to classification of lung cancer image database.