A genetic programming approach to feature construction for ensemble learning in skin cancer detection

Ensembles of classifiers have proved to be more effective than a single classification algorithm in skin image classification problems. Generally, the ensembles are created using the whole set of original features. However, some original features can be redundant and may not provide useful information in building good ensemble classifiers. To deal with this, existing feature construction methods that usually generate new features for only a single classifier have been developed but they fit the training data too well, resulting in poor test performance. This study develops a new classification method that combines feature construction and ensemble learning using genetic programming (GP) to address the above limitations. The proposed method is evaluated on two benchmark real-world skin image datasets. The experimental results reveal that the proposed algorithm has significantly outperformed two existing GP approaches, two state-of-the-art convolutional neural network methods, and ten commonly used machine learning algorithms. The evolved individual that is considered as a set of constructed features helps identify prominent original features which can assist dermatologists in making a diagnosis.