Ensemble of Artificial Bee Colony Optimization and Random Forest Technique for Feature Selection and Classification of Protein Function Family Prediction

This paper proposes the application of a swarm intelligence algorithm called Artificial Bee Colony (ABC) for the feature selection to feed a Random Forest (RF) classifier aiming to recognise Traffic Signs. In this paper, the authors define and assess several fitness functions for the feature selection stage. The idea is to minimise the correlation and maximise the entropy of a set of masks to be used for feature extraction results in a higher information gain and allows to reach recognition accuracies comparable with other state-of-art algorithms. The RF comprises as a committee based on decision trees, which allows handling large datasets and features with high performance, enabling a Traffic Sign Recognition (TSR) system oriented for real-time implementations. The German Traffic Sign Recognition Benchmark (GTSRB) was used for experiments, serving as a real basis for comparison of performance for the authors' proposal.

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Quantum artificial bee colony optimization algorithm based on Bloch coordinates of quantum bit

Journal of Computer Applications ◽

10.3724/sp.j.1087.2012.01935 ◽

2013 ◽

Vol 32 (6) ◽

pp. 1935-1938

Author(s):

Zheng-jun YI ◽

Rong-hua HE ◽

Kun HOU

Keyword(s):

Optimization Algorithm ◽

Artificial Bee Colony ◽

Quantum Bit ◽

Artificial Bee Colony Optimization ◽

Bee Colony ◽

Bee Colony Optimization

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Mining of structural motifs in proteins using artificial bee colony optimization framework for druggability

Journal of Bioinformatics and Computational Biology ◽

10.1142/s0219720021500256 ◽

2021 ◽

Author(s):

L. S. Suma ◽

S. S. Vinod Chandra

Keyword(s):

Artificial Bee Colony ◽

Area Under The Curve ◽

Docking Studies ◽

Artificial Bee Colony Optimization ◽

Optimization Framework ◽

Bee Colony ◽

Novel Variant ◽

Bee Colony Optimization ◽

Novel Strategy ◽

Sensitivity Specificity

In this work, we have developed an optimization framework for digging out common structural patterns inherent in DNA binding proteins. A novel variant of the artificial bee colony optimization algorithm is proposed to improve the exploitation process. Experiments on four benchmark objective functions for different dimensions proved the speedier convergence of the algorithm. Also, it has generated optimum features of Helix Turn Helix structural pattern based on the objective function defined with occurrence count on secondary structure. The proposed algorithm outperformed the compared methods in convergence speed and the quality of generated motif features. The motif locations obtained using the derived common pattern are compared with the results of two other motif detection tools. 92% of tested proteins have produced matching locations with the results of the compared methods. The performance of the approach was analyzed with various measures and observed higher sensitivity, specificity and area under the curve values. A novel strategy for druggability finding by docking studies, targeting the motif locations is also discussed.

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