Abstract
Feature selection (FS) methods are necessary to develop intelligent analysis tools that require data preprocessing and enhancing the performance of the machine learning algorithms. FS aims to maximize the classification accuracy by minimizing the number of selected features. This paper presents a new FS method using a modified Slime mould algorithm (SMA) based on the firefly algorithm (FA). In the developed SMAFA, FA is adopted to improve the exploration of SMA, since it has high ability to discover the feasible regions which have optima solution. This will lead to enhance the convergence by increasing the quality of the final output. SMAFA is evaluated using twenty UCI datasets and also with comprehensive comparisons to a number of the existing MH algorithms. To further assess the applicability of SMAFA, two high-dimensional datasets related to the QSAR modeling are used. Experimental results verified the promising performance of SMAFA using different performance measures.
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References
Bommert A, Sun X, Bischl B, Rahnenführer J, Lang M (2020) Benchmark for filter methods for feature selection in high-dimensional classification data. Comput Stat Data Anal 143:106839
Liu H, Motoda H (2012) Feature selection for knowledge discovery and data mining, vol 454. Springer, Berlin
Abualigah LM, Khader AT (2017) Unsupervised text feature selection technique based on hybrid particle swarm optimization algorithm with genetic operators for the text clustering. J Supercomput 73(11):4773–4795
Alhaj YA, **ang J, Zhao D, Al-Qaness MAA, Elaziz MA, Dahou A (2019) A study of the effects of stemming strategies on arabic document classification. IEEE Access 7:32664–32671
Abualigah LMQ (2019) Feature selection and enhanced krill herd algorithm for text document clustering. Springer, Berlin
Sun G, Li J, Dai J, Song Z, Lang F (2018) Feature selection for iot based on maximal information coefficient. Future Gener Comput Syst 89:606–616
AlHajri MI, Ali NT, Shubair RM (2019) Indoor localization for iot using adaptive feature selection: a cascaded machine learning approach. IEEE Antennas Wirel Propag Lett 18(11):2306–2310
Al-qaness MAA (2019) Device-free human micro-activity recognition method using wifi signals. Geo Spat Inf Sci 22(2):128–137
Lambin P, Rios-Velazquez E, Leijenaar R, Carvalho S, Van Stiphout RGPM, Granton P, Zegers CML, Gillies R, Boellard R, Dekker A et al (2012) Radiomics: extracting more information from medical images using advanced feature analysis. Eur J Cancer 48(4):441–446
Raj RJS, Shobana SJ, Pustokhina IV, Pustokhin DA, Gupta D, Shankar K (2020) Optimal feature selection-based medical image classification using deep learning model in internet of medical things. IEEE Access 8:58006–58017
Alomari OA, Khader AT, Al-Betar MA, Abualigah LM (2017) Gene selection for cancer classification by combining minimum redundancy maximum relevancy and bat-inspired algorithm. Int J Data Min Bioinform 19(1):32–51
Ibrahim RA, Oliva D, Ewees Amed A, Lu S (2017) Feature selection based on improved runner-root algorithm using chaotic singer map and opposition-based learning. In: International conference on neural information processing, Springer, pp 156–166
Li Y, Li T, Liu H (2017) Recent advances in feature selection and its applications. Knowl Inf Syst 53(3):551–577
Sharkawy RM, Ibrahim K, Salama MMA, Bartnikas R (2011) Particle swarm optimization feature selection for the classification of conducting particles in transformer oil. IEEE Trans Dielectr Electr Insul 18(6):1897–1907
Rao H, Shi X, Rodrigue AK, Feng J, **a Y, Elhoseny M, Yuan X, Lichuan G (2019) Feature selection based on artificial bee colony and gradient boosting decision tree. Appl Soft Comput 74:634–642
Sahlol AT, Kollmannsberger P, Ewees AA (2020) Efficient classification of white blood cell leukemia with improved swarm optimization of deep features. Sci Rep 10(1):1–11
Elaziz MEA, Ewees AA, Oliva D, Duan P, **ong S (2017) A hybrid method of sine cosine algorithm and differential evolution for feature selection. In: International conference on neural information processing, Springer, pp 145–155
Laith A, Ali D (2020) A comprehensive survey of the grasshopper optimization algorithm: results, variants, and applications. Neural Comput Appl 32:1–24
Das AK, Das S, Ghosh A (2017) Ensemble feature selection using bi-objective genetic algorithm. Knowl Based Syst 123:116–127
Mafarja M, Mirjalili S (2018) Whale optimization approaches for wrapper feature selection. Appl Soft Comput 62:441–453
Abualigah L, Diabat A, Mirjalili S, Elaziz MA, Gandomi AH (2020) The arithmetic optimization algorithm. Comput Methods Appl Mech Eng 376:113609
Laith A (2020) Group search optimizer: a nature-inspired meta-heuristic optimization algorithm with its results, variants, and applications. Neural Comput Appl 13:1–24
Al-Tashi Q, Rais HM, Jadid AS, Seyedali M, Hitham A (2020) A review of grey wolf optimizer-based feature selection methods for classification. Evolutionary machine learning techniques. Springer, Berlin, pp 273–286
Thaer T, Asghar HA, Majdi M, Song DJ, Seyedali M (2020) Binary harris hawks optimizer for high-dimensional, low sample size feature selection. Evolutionary machine learning techniques. Springer, Berlin, pp 251–272
Zawbaa HM, Emary E, Parv B, Sharawi M (2016) Feature selection approach based on moth-flame optimization algorithm. In: 2016 IEEE congress on evolutionary computation (CEC), IEEE, pp 4612–4617
Mafarja M, Qasem A, Heidari AA, Aljarah I, Faris H, Mirjalili S (2020) Efficient hybrid nature-inspired binary optimizers for feature selection. Cogn Comput 12(1):150–175
Ibrahim RA, Ewees AA, Oliva D, Elaziz MA, Songfeng L (2019) Improved salp swarm algorithm based on particle swarm optimization for feature selection. J Ambient Intell Humaniz Comput 10(8):3155–3169
Neggaz N, Ewees AA, Elaziz MA, Mafarja M (2020) Boosting salp swarm algorithm by sine cosine algorithm and disrupt operator for feature selection. Expert Syst Appl 145:113103
Ewees AA, Elaziz MA, Oliva D (2018) Image segmentation via multilevel thresholding using hybrid optimization algorithms. J Electron Imaging 27(6):063008
Mohamed A-B, Wei** D, Doaa E-S (2020) A hybrid harris hawks optimization algorithm with simulated annealing for feature selection. Artif Intell Rev 54:1–45
Li S, Chen H, Wang M, Heidari AA, Mirjalili S (2020) A new method for stochastic optimization. Slime Mould Algorithm Future Gener Comput Syst 111:300–323
Al-Qaness MAA, Fan H, Ewees AA, Yousri D, Elaziz MA (2021) Improved anfis model for forecasting wuhan city air quality and analysis COVID-19 lockdown impacts on air quality. Environ Res 194:110607
Yang X-S (2009) Firefly algorithms for multimodal optimization. In: International symposium on stochastic algorithms, Springer, pp 169–178
Yang X-S, He X (2013) Firefly algorithm: recent advances and applications. ar**v preprint. ar**v:1308.3898
El AMA, Ewees Ahmed A, Ella HA (2016) Hybrid swarms optimization based image segmentation. Hybrid soft computing for image segmentation. Springer, Berlin, pp 1–21
Jian Z, Atefeh N, Arslan CA, Thai PB, Mahdi H (2019) Novel approach for forecasting the blast-induced aop using a hybrid fuzzy system and firefly algorithm. Eng Comput 36:1–10
Aravind R, Modale Devesh R, Radha S (2020) Optimal scheduling of tasks in cloud computing using hybrid firefly-genetic algorithm. Advances in decision sciences, image processing, security and computer vision. Springer, Berlin, pp 678–687
Fateen Seif-Eddeen K, Adrián B-P (2014) Intelligent firefly algorithm for global optimization. Cuckoo search and firefly algorithm. Springer, Berlin, pp 315–330
Senthilnath J, Omkar SN, Mani V (2011) Clustering using firefly algorithm: performance study. Swarm Evol Comput 1(3):164–171
Selvakumar B, Muneeswaran K (2019) Firefly algorithm based feature selection for network intrusion detection. Comput Secur 81:148–155
Sawhney R, Mathur P, Shankar R (2018) A firefly algorithm based wrapper-penalty feature selection method for cancer diagnosis. In: International conference on computational science and its applications, Springer, pp 438–449
Marie-Sainte SL, Alalyani N (2020) Firefly algorithm based feature selection for Arabic text classification. J King Saud Univ Comput Inf Sci 32(3):320–328
Faris H, Aljarah I, Mirjalili S (2016) Training feedforward neural networks using multi-verse optimizer for binary classification problems. Appl Intell 45(2):322–332
Mafarja M, Aljarah I, Heidari AA, Hammouri AI, Faris H, Ala’M A-Z, Mirjalili S (2018) Evolutionary population dynamics and grasshopper optimization approaches for feature selection problems. Knowl Based Syst 145:25–45
Faris H, Mafarja MM, Heidari AA, Aljarah I, Ala’M A-Z, Mirjalili S, Fujita H (2018) An efficient binary salp swarm algorithm with crossover scheme for feature selection problems. Knowl Based Syst 154:43–67
Hammouri AI, Majdi M, Azmi A-BM, Awadallah MA, Iyad A-D (2020) An improved dragonfly algorithm for feature selection. Knowl Based Syst 203:106131
Pei H, Jeng-Shyang P, Shu-Chuan C (2020) Improved binary grey wolf optimizer and its application for feature selection. Knowl Based Syst 195:105746
Hegazy AhE, Makhlouf MA, El-Tawel GhS (2020) Improved salp swarm algorithm for feature selection. J King Saud Univ Comput Inf Sci 32(3):335–344
Tubishat M, Idris N, Shuib L, Abushariah MAM, Mirjalili S (2020) Improved salp swarm algorithm based on opposition based learning and novel local search algorithm for feature selection. Expert Syst Appl 145:113122
Faris H, Heidari AA, Al-Zoubi A, Mafarja M, Ibrahim A, Mohammed E, Seyedali M (2020) Time-varying hierarchical chains of salps with random weight networks for feature selection. Expert Syst Appl 140:112898
Gholami J, Pourpanah F, Wang X (2020) Feature selection based on improved binary global harmony search for data classification. Appl Soft Comput 93:106402
Aljarah I, Habib M, Faris H, Al-Madi N, Heidari AA, Mafarja M, Elaziz MA, Mirjalili S (2020) A dynamic locality multi-objective salp swarm algorithm for feature selection. Comput Ind Eng 147:106628
Malakar S, Ghosh M, Bhowmik S, Sarkar R, Nasipuri M (2020) A ga based hierarchical feature selection approach for handwritten word recognition. Neural Comput Appl 32(7):2533–2552
Mohamed EA, Ewees AA, Ibrahim RA, Songfeng L (2020) Opposition-based moth-flame optimization improved by differential evolution for feature selection. Math Comput Simul 168:48–75
Yang XS (2010) Nature-inspired metaheuristic algorithms. Luniver Press, London
Bache K, Lichman M (2013) UCI machine learning repository. University of California, Irvine, School of Information and Computer Sciences. http://archive.ics.uci.edu/ml
Mafarja M, Ibrahim A, Asghar HA, Hossam E, Fournier-Viger P, Li X, Mirjalili S (2018) Binary dragonfly optimization for feature selection using time-varying transfer functions. Knowl Based Syst 161:185–204
Emary E, Zawbaa HM, Hassanien AE (2016) Binary grey wolf optimization approaches for feature selection. Neurocomputing 172:371–381
Zhang H, Wang J, Sun Z, Zurada JM, Pal NR (2019) Feature selection for neural networks using group lasso regularization. IEEE Trans Knowl Data Eng 32(4):659–673
Arora S, Anand P (2019) Binary butterfly optimization approaches for feature selection. Expert Syst Appl 116:147–160
Mafarja M, Aljarah I, Faris H, Hammouri AI, Ala’M A-Z (2019) Binary grasshopper optimisation algorithm approaches for feature selection problems. Expert Syst Appl 117:267–286
Das A, Das S (2017) Feature weighting and selection with a pareto-optimal trade-off between relevancy and redundancy. Pattern Recogn Lett 88:12–19
Al-Thanoon NA, Qasim OS, Algamal ZY (2019) A new hybrid firefly algorithm and particle swarm optimization for tuning parameter estimation in penalized support vector machine with application in chemometrics. Chemom Intell Lab Syst 184:142–152
Al-Dabbagh ZT, Algamal ZY (2019) A robust quantitative structure-activity relationship modelling of influenza neuraminidase a/pr/8/34 (h1n1) inhibitors based on the rank-bridge estimator. SAR and QSAR in Environmental Research 30(6):417–428
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This project was supported financially by the Academy of Scientific Research and Technology (ASRT), Egypt, Grant 6619).
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Ewees, A.A., Abualigah, L., Yousri, D. et al. Improved Slime Mould Algorithm based on Firefly Algorithm for feature selection: A case study on QSAR model. Engineering with Computers 38 (Suppl 3), 2407–2421 (2022). https://doi.org/10.1007/s00366-021-01342-6
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DOI: https://doi.org/10.1007/s00366-021-01342-6