Affiliations: School of Electrical and Computer Engineering, Shiraz University, Shiraz, Iran
Correspondence:
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Corresponding author: Eghbal G. Mansoori, School of Electrical and Computer Engineering, Shiraz University, Shiraz, Iran. Tel.: +98 71 36133160; E-mail:mansoori@shirazu.ac.ir
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.
