Development of a Java Library with Bacterial Foraging Optimization for Feature Selection of High-Dimensional Data

Tessy Badriyah - Politeknik Elektronika Negeri Surabaya, Surabaya 60112, Indonesia
Iwan Syarif - Politeknik Elektronika Negeri Surabaya, Surabaya 60112, Indonesia
Fitriani Hardiyanti - PT. Sinergi Informatika Semen Indonesia, Gresik, 61122

Citation Format:



High-dimensional data allows researchers to conduct comprehensive analyses. However, such data often exhibits characteristics like small sample sizes, class imbalance, and high complexity, posing challenges for classification. One approach employed to tackle high-dimensional data is feature selection. This study uses the Bacterial Foraging Optimization (BFO) algorithm for feature selection. A dedicated BFO Java library is developed to extend the capabilities of WEKA for feature selection purposes. Experimental results confirm the successful integration of BFO. The outcomes of BFO's feature selection are then compared against those of other evolutionary algorithms, namely Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Artificial Bee Colony (ABC), and Ant Colony Optimization (ACO).  Comparison of algorithms conducted using the same datasets.  The experimental results indicate that BFO effectively reduces features while maintaining consistent accuracy. In 4 out of 9 datasets, BFO outperforms other algorithms, showcasing superior processing time performance in 6 datasets. BFO is a favorable choice for selecting features in high-dimensional datasets, providing consistent accuracy and effective processing. The optimal fraction of features in the Ovarian Cancer dataset signifies that the dataset retains a minimal number of selected attributes. Consequently, the learning process gains speed due to the reduced feature set. Remarkably, accuracy substantially increased, rising from 0.868 before feature selection to 0.886 after feature selection. The classification processing time has also been significantly shortened, completing the task in just 0.3 seconds, marking a remarkable improvement from the previous 56.8 seconds.


Feature Selection; High Dimensional Data; Bacterial Foraging Optimization; Evolutionary Algorithm

Full Text:



I. Guyon, S. Gunn, M. Nikravesh, and L. Zadeh, Feature extraction. Foundations and applications. Papers from NIPS 2003 workshop on feature extraction, Whistler, BC, Canada, December 11–13, 2003. With CD-ROM. 2006.

I. Guyon, J. Li, T. Mader, P. A. Pletscher, G. Schneider, and M. Uhr, "Competitive baseline methods set new standards for the NIPS 2003 feature selection benchmark," Pattern Recognition Letters, vol. 28, no. 12, pp. 1438-1444, 2007/09/01/ 2007, doi:

A. Jain and D. Zongker, "Feature selection: evaluation, application, and small sample performance," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 19, no. 2, pp. 153-158, 1997, doi: 10.1109/34.574797.

M. Kudo and J. Sklansky, "Comparison of algorithms that select features for pattern classifiers," Pattern Recognition, vol. 33, no. 1, pp. 25-41, 2000/01/01/ 2000, doi:

L. Ke, Z. Feng, and Z. Ren, "An efficient ant colony optimization approach to attribute reduction in rough set theory," Pattern Recognition Letters, vol. 29, no. 9, pp. 1351-1357, 2008/07/01/ 2008, doi:

D. Mittal and M. Bala, "Hybrid feature selection approach using bacterial foraging algorithm guided by Naive Bayes classification," in 2017 8th International Conference on Computing, Communication and Networking Technologies (ICCCNT), 3-5 July 2017 2017, pp. 1-7, doi: 10.1109/ICCCNT.2017.8204178.

B. Kumari and T. Swarnkar, "Filter versus wrapper feature subset selection in large dimensionality micro array: A review," International Journal of Computer Science and Information Technologies, vol. 2, pp. 1048-1053, 01/01 2011.

A. Kumar and A. K. Vishwakarma, "Multilevel Crop Image Segmentation using Bacterial Foraging Optimization Based on Minimum Cross Entropy," in 2021 International Conference on Control, Automation, Power and Signal Processing (CAPS), 10-12 Dec. 2021 2021, pp. 1-6, doi: 10.1109/CAPS52117.2021.9730680.

C. Zhang, J. Yu, and B. Niu, "Bacterial Foraging Optimization Based on Multi-colony Cooperation Strategy," in 2020 IEEE Symposium Series on Computational Intelligence (SSCI), 1-4 Dec. 2020 2020, pp. 1543-1548, doi: 10.1109/SSCI47803.2020.9308213.

F. Dubuisson, A. Chandra, M. Rezkallah, and H. Ibrahim, "A Bacterial Foraging Optimization Technique and Predictive Control Approach for Power Management in a Standalone Microgrid," in 2020 IEEE Electric Power and Energy Conference (EPEC), 9-10 Nov. 2020 2020, pp. 1-7, doi: 10.1109/EPEC48502.2020.9320038.

P. P. S. Subhashini, M. S. S. Ram, and D. S. Rao, "Bacterial Foraging Optimized Parameters for ANN using Adaptive Harris Hawks Weight Optimization," in 2021 6th International Conference on Inventive Computation Technologies (ICICT), 20-22 Jan. 2021 2021, pp. 849-854, doi: 10.1109/ICICT50816.2021.9358701.

S. Zhang, X. Ji, L. Guo, and Z. Bao, "Multi-objective bacterial foraging optimization algorithm based on effective area in cognitive emergency communication networks," China Communications, vol. 18, no. 12, pp. 252-269, 2021, doi: 10.23919/JCC.2021.12.016.

H. Chen, Q. Zhang, J. Luo, Y. Xu, and X. Zhang, "An enhanced Bacterial Foraging Optimization and its application for training kernel extreme learning machine," Applied Soft Computing, vol. 86, p. 105884, 2020/01/01/ 2020, doi:

B. Yang, X. Huang, W. Cheng, T. Huang, and X. Li, "Discrete bacterial foraging optimization for community detection in networks," Future Generation Computer Systems, vol. 128, pp. 192-204, 2022/03/01/ 2022, doi:

M. Kaur and S. Kadam, "A novel multi-objective bacteria foraging optimization algorithm (MOBFOA) for multi-objective scheduling," Applied Soft Computing, vol. 66, pp. 183-195, 2018/05/01/ 2018, doi:

W. Zhao and L. Wang, "An effective bacterial foraging optimizer for global optimization," Information Sciences, vol. 329, pp. 719-735, 2016/02/01/ 2016, doi:

L. Wang, W. Zhao, Y. Tian, and G. Pan, "A bare bones bacterial foraging optimization algorithm," Cognitive Systems Research, vol. 52, pp. 301-311, 2018/12/01/ 2018, doi:

P. Manikandan and D. Ramyachitra, "Bacterial Foraging Optimization –Genetic Algorithm for Multiple Sequence Alignment with Multi-Objectives," Scientific Reports, vol. 7, no. 1, p. 8833, 2017/08/18 2017, doi: 10.1038/s41598-017-09499-1.

X. Yan, Y. Zhu, H. Zhang, H. Chen, and B. Niu, "An Adaptive Bacterial Foraging Optimization Algorithm with Lifecycle and Social Learning," Discrete Dynamics in Nature and Society, vol. 2012, p. 409478, 2012/11/14 2012, doi: 10.1155/2012/409478.

Q. Zhang, H. Chen, J. Luo, Y. Xu, C. Wu, and C. Li, "Chaos Enhanced Bacterial Foraging Optimization for Global Optimization," IEEE Access, vol. 6, pp. 64905-64919, 2018, doi: 10.1109/ACCESS.2018.2876996.

J. Jiang, X. Xiong, Y. Ou, and H. Wang, "An Improved Bacterial Foraging Optimization with Differential and Poisson Distribution Strategy and its Application to Nurse Scheduling Problem," in Advances in Swarm Intelligence, vol. 12145: © Springer Nature Switzerland AG 2020., 2020, pp. 312-24.

Y. Chen and W. Lin, "An improved bacterial foraging optimization," in 2009 IEEE International Conference on Robotics and Biomimetics (ROBIO), 19-23 Dec. 2009 2009, pp. 2057-2062, doi: 10.1109/ROBIO.2009.5420524.

M. He, J. Chen, and H. Deng, "Bacterial Foraging Optimization Algorithm with Dimension by Dimension Improvement," in 2019 4th International Conference on Computational Intelligence and Applications (ICCIA), 21-23 June 2019 2019, pp. 1-5, doi: 10.1109/ICCIA.2019.00008.

H. Nouri and T. S. Hong, "Development of bacteria foraging optimization algorithm for cell formation in cellular manufacturing system considering cell load variations," Journal of Manufacturing Systems, vol. 32, no. 1, pp. 20-31, 2013/01/01/ 2013, doi:

D. Ramyachitra and V. Veeralakshmi, "Bacterial Foraging Optimization for protein structure prediction using FCC & HP energy model," Gene Reports, vol. 7, pp. 43-49, 2017/06/01/ 2017, doi:

B. Hernández-Ocaña, O. Chávez-bosquez, J. HernáNdez-Torruco, J. Canul-Reich, and P. Pozos-Parra, "Bacterial Foraging Optimization Algorithm for Menu Planning," IEEE Access, vol. 6, pp. 8619-8629, 2018, doi: 10.1109/ACCESS.2018.2794198.

G. Holmes, A. Donkin, and I. H. Witten, "WEKA: a machine learning workbench," in "Computer Science Working Papers," Working Paper 1994. [Online]. Available:

M. P. Kevin, "Bacterial Foraging Optimization," International Journal of Swarm Intelligence Research (IJSIR), vol. 1, no. 1, pp. 1-16, 2010.

Z. Zhu, Y.-S. Ong, and M. Dash, "Markov blanket-embedded genetic algorithm for gene selection," Pattern Recognition, vol. 40, no. 11, pp. 3236-3248, 2007/11/01/ 2007, doi:

"TurkishTextCategorizationProject - Browse /4. Zemberek-Stemmed at"