Abstract
Parkinson's disease is a neurodegenerative disease that affects millions of people around the world and cannot be cured fundamentally. Automatic identification of early Parkinson's disease on feature data sets is one of the most challenging medical tasks today. Many features in these datasets are useless or suffering from problems like noise, which affect the learning process and increase the computational burden. To ensure the optimal classification performance, this article proposes a hybrid feature selection algorithm based on an improved discrete artificial bee colony algorithm to improve the efficiency of feature selection. The algorithm combines the advantages of filters and wrappers to eliminate most of the uncorrelated or noisy features and determine the optimal subset of features. In the filter, three different variable ranking methods are employed to pre-rank the candidate features, then the population of artificial bee colony is initialized based on the significance degree of the re-rank features. In the wrapper part, the artificial bee colony algorithm evaluates individuals (feature subsets) based on the classification accuracy of the classifier to achieve the optimal feature subset. In addition, for the first time, we introduce a strategy that can automatically select the best classifier in the search framework more quickly. By comparing with several publicly available datasets, the proposed method achieves better performance than other state-of-the-art algorithms and can extract fewer effective features.
- Sigurlaug Sveinbjornsdottir. 2016. The clinical symptoms of Parkinson's disease. J. Neurochem. 139, 1 (2016), 318–324. DOI:http://doi.org/10.1111/jnc.13691Google Scholar
Cross Ref
- Michaela E. Johnson, Benjamin Stecher, Viviane Labrie, Lena Brundin, and Patrik Brundin. 2018. Triggers, facilitators, and aggravators: Redefining Parkinson's disease pathogenesis. Trends Neurosci. 42, 1 (2018), 4–13. DOI:http://doi.org/10.1016/j.tins.2018.09.007Google Scholar
Cross Ref
- Zafar Lqbal and Mathias Toft. 2019. TMEM230 variants in Parkinson's disease. Nature Genet. 51 (2019), 366. DOI:http://doi.org/10.1038/s41588-019-0353-7Google Scholar
Cross Ref
- Patricia C. Poluha, Hans-Leo Teulings, and Robert H. Brookshire. 1998. Handwriting and speech changes across the levodopa cycle in Parkinson's disease. Acta Psychologica 100, 1–2 (1998), 71–84. DOI:http://doi.org/10.1016/s0001-6918(98)00026-2Google Scholar
Cross Ref
- Sara Rosenblum, Margalit Samuel, Sharon Zlotnik, Ilana Erikh, and Ilana Schlesinger. 2013. Handwriting as an objective tool for Parkinson's disease diagnosis. J. Neurop. 260, 9 (2013), 2357–2361. DOI:http://doi.org/10.1007/s00415-013-6996-xGoogle Scholar
- Lucas S. Bernardo, Angeles Quezada, Roberto Munoz, Fernanda Martins Maia, Clayton R. Pereira, Wanqing Wu, and Victor Hugo C. de Albuquerque. 2019. Handwritten pattern recognition for early Parkinson's disease diagnosis. Pattern Recogn. Lett. 125 (2019), 78–84. DOI:http://doi.org/10.1016/j.patrec.2019.04.003Google Scholar
Cross Ref
- Clayton R. Pereira, Danilo R. Pereira, Francisco A. Silva, João P. Masieiro, Silke A. T. Weber, Christian Hook, and João P. Papa. 2016. A new computer vision-based approach to aid the diagnosis of Parkinson's disease. Comput. Methods Progr. Biomed. 136 (2016), 79–88. DOI:http://doi.org/10.1016/j.cmpb.2016.08.005 Google Scholar
Digital Library
- Clayton R. Pereira, Silke A. T. Weber, Christian Hook, Gustavo H. Rosa, and João P. Papa. 2011. Deep learning-aided Parkinson's disease diagnosis from handwritten dynamics. In Proceedings of the 29th SIBGRAPI Conference on Graphics, Patterns and Images (SIBGRAPI’16), IEEE Press, New York, NY, 340–346. DOI:http://doi.org/10.1109/SIBGRAPI.2016.054Google Scholar
- Yiming Yang and Jan O. Pedersen. 1998. A comparative study on feature selection in text categorization. In Proceedings of the 14th International Conference on Machine Learning, IEEE Press, New York, NY, 9 pages. 412–420. DOI:http://doi.org/10.4156/AISS.vol4.issue3.3 Google Scholar
Digital Library
- Fernando Jimenez, Carlos Martinez, Enrico Marzano, Jose Tomas Palma, Gracia Sánchez, and Guido Sciavicco. 2019. Multiobjective evolutionary feature selection for fuzzy classification. IEEE Trans. Fuzzy Syst. 27, 5 (2019), 1085–1099. DOI:http://doi.org/10.1109/TFUZZ.2019.2892363Google Scholar
Cross Ref
- Xuelian Dent, Yuqing Li, Jian Weng, and Jilian Zhang. 2019. Feature selection for text classification: A review. Multimedia Tools Appl. 78 (2019), 3797–3816. DOI:http://doi.org/10.1007/s11042-018-6083-5 Google Scholar
Digital Library
- Xiangyang Wang, Jie Yang, Xiaolong Teng, Weijun Xia, and Richard Jensen. 2007. Feature selection based on rough sets and particle swarm optimization. Pattern Recogn. Lett. 28, 4 (2007), 459–471. DOI:http://doi.org/10.1016/j.patrec.2006.09.003 Google Scholar
Digital Library
- Bin Xue, Mengjie Zhang, and Will N. Browne. 2013. Particle swarm optimization for feature selection in classification: A multi-objective approach. IEEE Trans. Cybernet. 43, 6 (2013), 1656–1671. DOI:http://doi.org/10.1109/TSMCB.2012.2227469Google Scholar
Cross Ref
- Binh Tran, Bing Xue, and Mengjie Zhang. 2019. Variable-length particle swarm optimization for feature selection on high-dimensional classification. IEEE Trans. Evolution. Comput. 23, 3 (2019), 473–487. DOI:http://doi.org/10.1109/TEVC. 2018.2869405Google Scholar
Cross Ref
- Ching Sheng Ooi, Meng HeeLim, and M. Salman Leong. 2019. Self-Tune linear adaptive-genetic algorithm for feature selection. IEEE/Access 7 (2019) 138211–138232. DOI:http://doi.org/10.1109/ACCESS.2019.2942962Google Scholar
- Yingtong Wang, Jiandong Wang, Hao Liao, and Haiyan Chen. 2017. Unsupervised feature selection based on Markov blanket and particle swarm optimization. J. Syst. Eng. and Electronics 28, 1 (2017), 151–161. DOI:http://doi.org/10.21629/JSEE.2017.01.17Google Scholar
Cross Ref
- Yong Zhang, Dunwei Gong, and Jian Cheng. 2017. Multi-objective particle swarm optimization approach for cost-based feature selection in classification. IEEE/ACM Trans. Comput. Biol. Bioinform. 14, 1 (2017), 64–75. DOI:http://doi.org/10.1109/TCBB.2015.2476796 Google Scholar
Digital Library
- Tusongjiang Kari, Wensheng Gao, Dongbo Zhao, Kaherjiang Abiderexiti, Wenxiong Mo, Yong Wang, and Le Luan. 2018. Hybrid feature selection approach for power transformer fault diagnosis based on support vector machine and genetic algorithm. IET Gen. Transmiss. Distrib. 12, 21 (2018), 5672–5680. DOI:http://doi.org/10.1049/iet-gtd.2018.5482Google Scholar
Cross Ref
- Qasem Al-Tashi, Said Jadid Abdul Kadir, Helmi Md Rais, Seyedali Mirjalili, and Hitham Alhussian. 2019. Binary optimization using hybrid grey wolf optimization for feature selection. IEEE Access 7 (2019), 39496–39508. DOI:http://doi.org/10.1109/ACCESS.2019.2906757Google Scholar
Cross Ref
- Penying Tao, Zhe Sun, and Zhixin Sun. 2018. An improved intrusion detection algorithm based on GA and SVM. IEEE Access 6 (2018), 13624–13631. DOI:http://doi.org/10.1109/ACCESS.2018.2810198Google Scholar
Cross Ref
- Binh Tran, Bing Xue, and Mengjie Zhang. 2018. A new representation in PSO for discretization-based feature selection. IEEE Trans. Cybernet. 48, 6 (2018), 1733–1746. DOI:http://doi.org/10.1109/TCYB.2017.2714145Google Scholar
Cross Ref
- Kamlesh. Mistry, Li Zhang, Neoh Siewchin, Lim Pengchee, and Ben Fielding. 2017. A micro-GA embedded PSO feature selection approach to intelligent facial emotion recognition. IEEE Trans. Cybernet. 47, 6 (2017), 1496–1509. DOI:http://doi.org/10.1109/TCYB.2016.2549639Google Scholar
Cross Ref
- Yu Xue, Tao Tang, and Alex Liu. 2019. Large-scale feedforward neural network optimization by a self-adaptive strategy and parameter-based particle swarm optimization. IEEE Access 7 (2019), 52473–52483. DOI:http://doi.org/10.1109/ACCESS.2019.2911530Google Scholar
Cross Ref
- Akkasi Abbas and Varoğlu Ekrem. 2017. Improving biochemical named entity recognition using PSO Classifier selection and bayesian combination methods. IEEE/ACM Trans. Comput. Biol. Bioinform. 14, 6 (2017), 1327–1338. DOI:http://doi.org/10.1109/TCBB.2016.2570216 Google Scholar
Digital Library
- Yordanos Kassa Semero, Jianhua Zhang, and Dehua Zheng. 2017. PV power forecasting using an integrated GA-PSO-ANFIS approach and gaussian process regression-based feature selection strategy. CSEE J. Power Energy Syst. 4, 2 (2017), 210–218. DOI:http://doi.org/10.17775/CSEEJPES.2016.01920Google Scholar
Cross Ref
- Dervis Karaboga. 2005. An idea based on honeybee swarm for numerical optimization. Erciyes Univ., Kayseri, Turkey, Technical Report No. TR06, 200 (2005), 1–10.Google Scholar
- Luping Zhou, Lei Wang, and Chunhua Shen. 2010. Feature selection with redundancy-constrained class separability. IEEE Trans. Neural Netw. 21, 5 (2010), 853–858. DOI:http://doi.org/10.1109/TNN.2010.2044189 Google Scholar
Digital Library
- Leily Sheugh and Sasan H. Alizadeh. 2015. A note on Pearson correlation coefficient as a metric of similarity in recommender systems. In Proceeding of the AI & Robotics Conference (IRANOPEN’15), IEEE Press, 1–6. DOI:http://doi.org/10.1109/RIOS.2015.7270736Google Scholar
- Igor Kononenko. 1994. Estimating attributes: Analysis and extensions of RELIEF. In Proceedings of the European Conference on Machine Learning. Springer Press, Berlin, 171–182. DOI: http://doi.org/10.1007/3-540-57868-4_57 Google Scholar
Digital Library
- Lizbeth Naranjo, Carlos J. Perez, Jacinto Martin, and Yolanda Campos-Roca. 2017. A two-stage variable selection and classification approach for Parkinson's disease detection by using voice recording replication. Comput. Methods Programs Biomed. 142 (2017), 147–156. DOI:http://doi.org/10.1016/j.cmpb.2017.02.019 Google Scholar
Digital Library
- C. Okan Sakar, Gorkem Serbes, Aysegul Gunduz, Hunkar C. Tunc, Hatice Nizam, Betul Erdogdu Sakar, Melih Tutuncu, Tarkan Aydin, M. Erdem Isenkul, and Hulya Apaydin. 2019. A comparative analysis of speech signal processing algorithms for Parkinson's disease classification and the use of the tunable Q-factor wavelet transform. Appl. Soft Comput. 74 (2019), 255–263. DOI:http://doi.org/10.1016/j.asoc.2018.10.022Google Scholar
Cross Ref
- Betul Erdogdu Sakar, M. Erdem Isenkul, C. Okan Sakar, Ahmet Sertbas, Fikret Gurgen, Sakir Delil, Hulya Apaydin, and Olcay Kursun. 2013. Collection and analysis of a Parkinson speech dataset with multiple types of sound recordings. IEEE J. Biomed. Health Inform. 17, 4 (2013), 828–834. DOI:http://doi.org/10.1109/JBHI.2013.2245674Google Scholar
Cross Ref
- Yuhui Shi and Russell C. Eberhart. 1998. A modified particle swarm optimizer. In Proceedings of the IEEE World Congress on Computational Intelligence, IEEE Press, 69–73. DOI: http://doi.org/10.1109/ICEC.1998.699146Google Scholar
- Eid Emary, Hossam M. Zawbaa, and Aboul. Ella Hassanien. 2016. Binary gray wolf optimization approaches for feature selection. Neurocomputing 172, 8 (2016), 371–381. DOI:http://doi.org/10.1016/j.neucom.2015.06.083 Google Scholar
Digital Library
- Davis Lawrence. 1991. Handbook of Genetic Algorithms. Van Nostrand Reinhold, New York, NY.Google Scholar
- Jun Sun, Wenbo Xu, and Bin Feng. 2004. A global search strategy of quantum-behaved particle swarm optimization. In Proceedings of the IEEE International Conference on Cybernetic Intelligence Systems. IEEE Press, New York, NY, 111–116. DOI:http://doi.org/10.1109/ICCIS.2004.1460396Google Scholar
- Rainer Storn and Kenneth Price. 1997. Differential evolution—A simple and efficient heuristic for global optimization over continuous spaces. J. Global Optim. 11, 4 (1997), 341–359. DOI:http://doi.org/10.1023/a:1008202821328 Google Scholar
Digital Library
- Xinshe Yang. 2010. Nature-inspired meta heuristic algorithm. Luniver Press, Beckington.Google Scholar
- Xinshe Yang. A new metaheuristic bat-inspired algorithm. In Proceedings of the Conference on Nature Inspired Cooperative Strategies for Optimization (NISCO’10), Springer Press, Berlin, 65–74. DOI:http://doi.org/10.1007/978-3-642-12538-6_6Google Scholar
- Ying Tan and Yuanchun Zhu. 2010. Fireworks algorithm for optimization. In Proceedings of the International Conference on Advances in Swarm Intelligence (ICSI’10). 355–364. DOI: http://doi.org/10.1007/978-3-642-13495-1_44 Google Scholar
Digital Library
- Dan Simon. 2008. Biogeography-based optimization. IEEE Trans. Evolution. Comput. 12, 6 (2008), 702–713. DOI:http://doi.org/10.1109/TEVC.2008.919004 Google Scholar
Digital Library
Index Terms
A Hybrid Feature Selection Algorithm Based on a Discrete Artificial Bee Colony for Parkinson's Diagnosis
Recommendations
A Novel Artificial Bee Colony Algorithm
IHMSC '14: Proceedings of the 2014 Sixth International Conference on Intelligent Human-Machine Systems and Cybernetics - Volume 01Artificial bee colony algorithm is a new population-based evolutionary method based on the intelligent behavior of honey bee swarm. It has shown more effective than other biological-inspired algorithms. However, there are still insufficiencies in ABC ...
Linear Weighted Gbest-Guided Artificial Bee Colony Algorithm
ISCID '12: Proceedings of the 2012 Fifth International Symposium on Computational Intelligence and Design - Volume 02Artificial bee colony (ABC) algorithm invented recently by Karaboga is a competitive stochastic population-based optimization algorithm. However, solution search equation used in the original ABC algorithm is good at exploration but poor at ...
Two modified versions of artificial bee colony algorithm
Artificial Bee Colony (ABC) algorithm is one of the most recently introduced swarm-based algorithms used in optimization problems. ABC simulates the intelligent foraging behavior of a honeybee swarm. In this paper, two aspects of ABC algorithm are ...






Comments