PARALLEL SOLUTION OF FEATURES SUBSET SELECTION PROCESS FOR HAND-PRINTED CHARACTER RECOGNITION
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Volume 2 (2), December 2019, Pages 170-177
Elviz Ismayilov, Rahman Mammadov
The existence of a huge amount of features for pattern recognition problems brings to the overloading of the training and exploitation steps of the recognition; also, highly correlated features affect the accuracy of the designed systems negatively. One of the most used ways for tackling this problem is the application of genetic algorithms for the solution of the binary optimization problems that appeared during the features subset selection process. In this paper was used parallel genetic algorithms for the selection of the most informative features in Azerbaijani hand-printed character recognition system by using opportunities of the distributed cluster computing. In this way after the given number of generations most appropriate features with the high recognition rate were selected from the features database.
feature selection; genetic algorithms; crossover methods; cluster computing; distributed systems
Ali, A. A. A., & Suresha, M. (2019, Oct). A novel features and classifiers fusion technique for recognition of Arabic handwritten character script [Article]. Sn Applied Sciences, 1(10), 13, Article Unsp 1286. https://doi.org/10.1007/s42452-019-1294-6
Baliarsingh, S. K., Ding, W. P., Vipsita, S., & Bakshi, S. (2019, Dec). A memetic algorithm using emperor penguin and social engineering optimization for medical data classification [Article]. Applied Soft Computing, 85, 15, Article 105773. https://doi.org/10.1016/j.asoc.2019.105773
Baniya, B. K., & Gnimpieba, E. Z. (2020). The Effectiveness of Distinctive Information for Cancer Cell Analysis Through Big Data [Proceedings Paper]. Advances in Computer Vision, Vol 2, 944, 57-68. https://doi.org/10.1007/978-3-030-17798-0_7
Barbuti, N., & Caldarola, T. (2018). An Innovative Multifunction System for Text Recognition of Digital Resources Reproducing Ancient Handwritten and Hand-Printed Artifacts. Assoc Computing Machinery. https://doi.org/10.1145/3240117.3240141
Benchaou, S., Nasri, M., & El Melhaoui, O. (2018, Jul). Feature Selection Based on Evolution Strategy for Character Recognition [Article]. International Journal of Image and Graphics, 18(3), 13, Article 1850014. https://doi.org/10.1142/s0219467818500146
Bin Ahmed, S., Naz, S., Razzak, M. I., & Yusof, R. (2019, Jan). Arabic Cursive Text Recognition from Natural Scene Images [Article]. Applied Sciences-Basel, 9(2), 27, Article 236. https://doi.org/10.3390/app9020236
Cilia, N. D., De Stefano, C., Fontanella, F., & di Freca, A. S. (2019, Apr). A ranking-based feature selection approach for handwritten character recognition [Article; Proceedings Paper]. Pattern Recognition Letters, 121, 77-86. https://doi.org/10.1016/j.patrec.2018.04.007
El Bahi, H., & Zatni, A. (2019, Sep). Text recognition in document images obtained by a smartphone based on deep convolutional and recurrent neural network [Article]. Multimedia Tools and Applications, 78(18), 26453-26481. https://doi.org/10.1007/s11042-019-07855-z
Ghosh, M., Guha, R., Singh, P. K., Bhateja, V., & Sarkar, R. (2019, Dec). A histogram based fuzzy ensemble technique for feature selection [Article]. Evolutionary Intelligence, 12(4), 713-724. https://doi.org/10.1007/s12065-019-00279-6
Harandi, F. A., Derhami, V., & Jamshidi, F. (2019, Dec). A new feature selection method based on task environments for controlling robots [Article]. Applied Soft Computing, 85, 13, Article 105812. https://doi.org/10.1016/j.asoc.2019.105812
Ismayilov, E. A. (2018). STUDY OF AZERBAIJANI HAND-PRINTED CHARACTERS RECOGNITION SYSTEM BY NEW FEATURE CLASS AND SVM METHOD. Problems of Information Technology, 89-94.
Ismayilova, N., & Ismayilov, E. (2018). “SOFT” FEATURES AND SVM FOR HAND-PRINTED CAHARCTERS RECOGNITION [Proceedings Paper]. Proceedings of the 6th International Conference on Control and Optimization with Industrial Applications, Vol Ii, 178-180.
Joan, S. P. F., & Valli, S. (2019, Mar). A Survey on Text Information Extraction from Born-Digital and Scene Text Images [Review]. Proceedings of the National Academy of Sciences India Section a-Physical Sciences, 89(1), 77-101. https://doi.org/10.1007/s40010-017-0478-y
Kouchami-Sardoo, I., Shirani, H., Esfandiarpour-Boroujeni, I., Alvaro-Fuentes, J., & Shekofteh, H. (2019, Nov). Optimal feature selection for prediction of wind erosion threshold friction velocity using a modified evolution algorithm [Article]. Geoderma, 354, 12, Article 113873. https://doi.org/10.1016/j.geoderma.2019.07.031
Kowsalya, S., & Periasamy, P. S. (2019, Sep). Recognition of Tamil handwritten character using modified neural network with aid of elephant herding optimization [Article]. Multimedia Tools and Applications, 78(17), 25043-25061. https://doi.org/10.1007/s11042-019-7624-2
Kumar, S., & Ieee. (2016). A Study for Handwritten Devanagari Word Recognition. Ieee.
Liu, H., & Ditzler, G. (2019, Aug). A semi-parallel framework for greedy information-theoretic feature selection [Article]. Information Sciences, 492, 13-28. https://doi.org/10.1016/j.ins.2019.03.075
Liu, H., Duan, Z., Wu, H. P., Li, Y. F., & Dong, S. Y. (2019, Dec). Wind speed forecasting models based on data decomposition, feature selection and group method of data handling network [Article]. Measurement, 148, 12, Article Unsp 106971. https://doi.org/10.1016/j.measurement.2019.106971
Liu, X. Y., Meng, G. F., & Pan, C. H. (2019, Jun). Scene text detection and recognition with advances in deep learning: a survey [Article]. International Journal on Document Analysis and Recognition, 22(2), 143-162. https://doi.org/10.1007/s10032-019-00320-5
Qiu, C. Y. (2019, Dec). A novel multi-swarm particle swarm optimization for feature selection [Article]. Genetic Programming and Evolvable Machines, 20(4), 503-529. https://doi.org/10.1007/s10710-019-09358-0
Rachmani, E., Hsu, C. Y., Nurjanah, N., Chang, P. W., Shidik, G. F., Noersasongko, E., Jumanto, J., Fuad, A., Ningrum, D. N. A., Kurniadi, A., & Lin, M. C. (2019, Dec). Developing an Indonesia’s health literacy short-form survey questionnaire (HLS-EU-SQ10-IDN) using the feature selection and genetic algorithm [Article]. Computer Methods and Programs in Biomedicine, 182, 10, Article Unsp 105047. https://doi.org/10.1016/j.cmpb.2019.105047
Roy, P. P., Bhunia, A. K., Bhattacharyya, A., & Pal, U. (2019, Mar). Word searching in scene image and video frame in multi-script scenario using dynamic shape coding [Article]. Multimedia Tools and Applications, 78(6), 7767-7801. https://doi.org/10.1007/s11042-018-6484-5
Shaukat, A., Farhan, S., Fahiem, M. A., Tauseef, H., Tahir, F., & Usman, G. (2018, Nov). Textural and Geometrical Features Based Approach for Identification of Individuals Using Palmprint and Hand Shape Images from Multiple Multimodal Datasets [Article]. Journal of Testing and Evaluation, 46(6), 2281-2298. https://doi.org/10.1520/jte20160625
Sun, J., Zhou, M. J., Ai, W. G., & Li, H. (2019, Dec). Dynamic prediction of relative financial distress based on imbalanced data stream: from the view of one industry [Article]. Risk Management-an International Journal, 21(4), 215-242. https://doi.org/10.1057/s41283-018-0047-y
Szendrei, R., Elek, I., & Marton, M. (2011). Graph-Based Feature Recognition of Line-Like Topographic Map Symbols. In Y. Tan, Y. Shi, Y. Chai, & G. Wang (Eds.), Advances in Swarm Intelligence, Pt Ii (Vol. 6729, pp. 291-298). Springer-Verlag Berlin.
Tsamardinos, I., Borboudakis, G., Katsogridakis, P., Pratikakis, P., & Christophides, V. (2019, Feb). A greedy feature selection algorithm for Big Data of high dimensionality [Article]. Machine Learning, 108(2), 149-202. https://doi.org/10.1007/s10994-018-5748-7
Umbarkar, A. J., & Sheth, P. D. (2015). CROSSOVER OPERATORS IN GENETIC ALGORITHMS: A REVIEW. ICTACT journal on soft computing, 6(1).
Venkataramana, L., Jacob, S. G., Ramadoss, R., Saisuma, D., Haritha, D., & Manoja, K. (2019, Nov). Improving classification accuracy of cancer types using parallel hybrid feature selection on microarray gene expression data [Article]. Genes & Genomics, 41(11), 1301-1313. https://doi.org/10.1007/s13258-019-00859-x
Wang, J. L., Lu, Y. H., Liu, J. B., & Quan, L. (2017, Oct). A robust three-stage approach to large-scale urban scene recognition [Article]. Science China-Information Sciences, 60(10), 13, Article 103101. https://doi.org/10.1007/s11432-017-9178-8
Wang, X., Feng, X., & Xia, Z. (2019, Oct). Scene video text tracking based on hybrid deep text detection and layout constraint [Article]. Neurocomputing, 363, 223-235. https://doi.org/10.1016/j.neucom.2019.05.101
Wang, Y. W., & Feng, L. Z. (2019, Dec). A new hybrid feature selection based on multi-filter weights and multi-feature weights [Article]. Applied Intelligence, 49(12), 4033-4057. https://doi.org/10.1007/s10489-019-01470-z
Zandieh, M., & Aslani, B. (2019, Dec). A hybrid MCDM approach for order distribution in a multiple-supplier supply chain: A case study [Article]. Journal of Industrial Information Integration, 16, 13, Article 100104. https://doi.org/10.1016/j.jii.2019.08.002
Zmyzgova, T. R., & Ieee. (2018). Special Features of Structural Pattern Recognition for Digital Images of Reactions of Integral Strain Gauges Indications. Ieee.