A novel density-based ensemble learning algorithm with application to protein structural classification

Abstract

Ensemble learning is an effective technique in classifying high-dimensional data such as bioinformatics sequences since it combines some learning models to improve the overall prediction accuracy. The key point in success of an ensemble algorithm is to build a set of diverse classifiers. In this regard, a novel density-based lazy stacking algorithm, called DBLS, is proposed in this paper. It takes the advantages of both lazy learning, in finding local optimal solutions, and the stacking method, in achieving classifier diversity, to obtain better performance while keeping the complexity intact. DBLS uses a stacking framework with lazy local learners based on density for building an ensemble of classifiers to predict the structural classes of proteins. To evaluate the performance of DBLS, it is compared against four rival classification methods. For this purpose, some real-world UCI datasets beside to three benchmark protein datasets are used in the experiments. The experimental results confirmed that DBLS significantly (with 95% confidence) outperforms other methods in terms of classification accuracy; over 3% advantage in absolute accuracy.