Effective Classification Using Artificial Bee Colony Based Feature Selector
Keywords:Artificial Bee Colony, Image Processing, Classification Technique, Feature Selection,
AbstractPepper classification is an important step in measuring the quality of the pepper. As of today, Malaysia Pepper Board (MPB) performed its classification task by doing a semi-automated process; where a commercial colour sorter is used to differentiate and categorized the peppers into respective groups. However, it is proven to be a challenging and time consuming process This paper introduces an effective pepper classification by applying a feature selection method in Artificial Bee Colony (ABC) algorithm. ABC algorithm is a metaheuristic algorithm inspired by the collective behavior of bees, which has been used successfully to solve complex problems of optimization. From this study, the proposed ABC algorithm that incorporates feature selection method resulted an accuracy of 92% tested with a small sample size. Meanwhile, 89.7% of accuracy was obtained with a larger sample size and a set of Red Mean; Green Median; Red Standard Deviation; Solidity Ratio; and Contrast were chosen as the best optimal features' subset.
J. Wong, Community, Jun. 2016. Retrieved from The Star Online: http://www.thestar.com.my/metro/community/2016/06/08/lucrativepepper-farming-prices-soar-as-more-people-use-the-spice-in-foodprocessing-and-manufacturi/
N. Yusup, D. Awang Iskandar, and S. Hassan, “Classifying Piper Nigrum using Artificial Bee Colony Algorithm,” 2014 4th International Conference on Artificial Intelligence with Applications in Engineering and Technology, 2014
A. Jain, “Data Clustering: 50 Years Beyond K-Means,” Pattern Recognition Letters, pp. 651-666, 2010.
U. Stańczyk and L. C. Jain, Feature Selection for Data and Pattern Recognition, Springer Berlin Heidelberg, 2015.
A.-C. Liew, H. Yan, and M. Yang, “Pattern Recognition Techniques for the Emerging Field of Bioinformatics: A Review,” Pattern Recognition, pp. 2055-2073, 2005.
I. Jeffrey, D. G. Higgins, and A. C. Culhane, “Comparison and evaluation of methods for generating differentially expressed gene list from microarray data,” BMC Bioinformatics, pp. 1471-2105, 2006.
B. Subanya, and R. Rajalaxmi, “Feature Selection using Artificial Bee Colony for Cardiovascular Disease Classification,” 2014 International Conference on Electronics and Communication System (lCECS), pp. 1- 6, 2014. IEEE.
T. Piatrik and E. Izquierdo, “Subspace clustering of images using Ant colony Optimisation,” 2009 16th IEEE International Conference on Image Processing (ICIP), Cairo, pp. 229-232, 2009.
N. Abd-Alsabour and M. Randall, “Feature Selection for Classification Using an Ant Colony System,” 2010 Sixth IEEE International Conference on e-Science Workshops, Brisbane, pp. 86-91, 2010.
A. Rani and R. Rajalaxmi, “Unsupervised feature selection using binary bat algorithm,” 2015 2nd International Conference on Electronics and Communication Systems (ICECS), Coimbatore, pp. 451-456, 2015.
H. Zawbaa, E. Emary, and B. Parv, “Feature selection approach based on moth-flame optimization algorithm,” 2016 IEEE Congress on Evolutionary Computation (CEC), Vancouver, pp. 4612-4617, 2016.
M. Pal and G. M. Foody, “Feature Selection for Classification of Hyperspectral Data by SVM,” IEEE Transactions on Geoscience and Remote Sensing, pp. 2297 - 2307, 2010. IEEE.
B. Hu, Y. Dai, and Y. Su, “Feature Selection for Optimized Highdimensional Biomedical Data using the Improved Shuffled Frog Leaping Algorithm,” IEEE/ACM Transactions on Computational Biology and Bioinformatics, 2016.
D. Wu, W. Yu, and Z. Yin, “Parameter estimation of rational models based on artificial bee colony algorithm,” Proc. of the International Conference on Modelling, Identification and Control (ICMIC), pp. 219-224, 2011.
D. Karaboga, “Artificial bee colony algorithm,” Scholarpedia, p. 6915, 2010.
D. Karaboga and B. Basturk, “A powerful and efficient algorithm fornumerical function optimization: artificial bee colony (abc) algorithm,” Journal of Global Optimization, pp. 459-471, 2007.
G. M. Foody, Sample Size Determination for Image Classification Accuracy. International Journal of Remote Sensing, Taylor & Francis, pp. 5273 – 5291, 2009.
M. Karunyalakshmi and N. Tajunisha, “Classification of Cancer Datasets using Artificial Bee Colony and Deep Feed Forward Neural Networks,” International Journal of Advanced Research in Computer and Communication Engineering, 2017
How to Cite
TRANSFER OF COPYRIGHT AGREEMENT
The manuscript is herewith submitted for publication in the Journal of Telecommunication, Electronic and Computer Engineering (JTEC). It has not been published before, and it is not under consideration for publication in any other journals. It contains no material that is scandalous, obscene, libelous or otherwise contrary to law. When the manuscript is accepted for publication, I, as the author, hereby agree to transfer to JTEC, all rights including those pertaining to electronic forms and transmissions, under existing copyright laws, except for the following, which the author(s) specifically retain(s):
- All proprietary right other than copyright, such as patent rights
- The right to make further copies of all or part of the published article for my use in classroom teaching
- The right to reuse all or part of this manuscript in a compilation of my own works or in a textbook of which I am the author; and
- The right to make copies of the published work for internal distribution within the institution that employs me
I agree that copies made under these circumstances will continue to carry the copyright notice that appears in the original published work. I agree to inform my co-authors, if any, of the above terms. I certify that I have obtained written permission for the use of text, tables, and/or illustrations from any copyrighted source(s), and I agree to supply such written permission(s) to JTEC upon request.