Improved Slime Mould Algorithm based on Firefly Algorithm for feature selection: A case study on QSAR model

[1]  A. Asuncion,et al.  UCI Machine Learning Repository, University of California, Irvine, School of Information and Computer Sciences , 2007 .

[2]  Laith Mohammad Abualigah,et al.  Unsupervised text feature selection technique based on hybrid particle swarm optimization algorithm with genetic operators for the text clustering , 2017, The Journal of Supercomputing.

[3]  Songfeng Lu,et al.  Opposition-based moth-flame optimization improved by differential evolution for feature selection , 2020, Math. Comput. Simul..

[4]  Doaa El-Shahat,et al.  A hybrid Harris Hawks optimization algorithm with simulated annealing for feature selection , 2020, Artificial Intelligence Review.

[5]  M. Mildner,et al.  Re-epithelialization and immune cell behaviour in an ex vivo human skin model , 2020, Scientific Reports.

[6]  Swagatam Das,et al.  Feature weighting and selection with a Pareto-optimal trade-off between relevancy and redundancy , 2017, Pattern Recognit. Lett..

[7]  Bernd Bischl,et al.  Benchmark for filter methods for feature selection in high-dimensional classification data , 2020, Comput. Stat. Data Anal..

[8]  Hossam Faris,et al.  A dynamic locality multi-objective salp swarm algorithm for feature selection , 2020, Comput. Ind. Eng..

[9]  Huiling Chen,et al.  Slime mould algorithm: A new method for stochastic optimization , 2020, Future Gener. Comput. Syst..

[10]  Ali Diabat,et al.  A comprehensive survey of the Grasshopper optimization algorithm: results, variants, and applications , 2020, Neural Computing and Applications.

[11]  Hossam Faris,et al.  Time-varying hierarchical chains of salps with random weight networks for feature selection , 2020, Expert Syst. Appl..

[12]  Patrick Granton,et al.  Radiomics: extracting more information from medical images using advanced feature analysis. , 2012, European journal of cancer.

[13]  V. Mani,et al.  Clustering using firefly algorithm: Performance study , 2011, Swarm Evol. Comput..

[14]  Pei Hu,et al.  Improved Binary Grey Wolf Optimizer and Its application for feature selection , 2020, Knowl. Based Syst..

[15]  Mohammed Abdulaziz Aide Al-qaness,et al.  Device-free human micro-activity recognition method using WiFi signals , 2019, Geo spatial Inf. Sci..

[16]  Hossam Faris,et al.  Binary grasshopper optimisation algorithm approaches for feature selection problems , 2019, Expert Syst. Appl..

[17]  Jacek M. Zurada,et al.  Feature Selection for Neural Networks Using Group Lasso Regularization , 2020, IEEE Transactions on Knowledge and Data Engineering.

[18]  Vahid Rafe,et al.  Using memetic algorithm for robustness testing of contract-based software models , 2020, Artificial Intelligence Review.

[19]  Mohamed Elhoseny,et al.  Feature selection based on artificial bee colony and gradient boosting decision tree , 2019, Appl. Soft Comput..

[20]  Adrian Bonilla-Petriciolet,et al.  Intelligent Firefly Algorithm for Global Optimization , 2014 .

[21]  Tao Li,et al.  Recent advances in feature selection and its applications , 2017, Knowledge and Information Systems.

[22]  Songfeng Lu,et al.  Improved salp swarm algorithm based on particle swarm optimization for feature selection , 2018, Journal of Ambient Intelligence and Humanized Computing.

[23]  M. M. A. Salama,et al.  Particle swarm optimization feature selection for the classification of conducting particles in transformer oil , 2011, IEEE Transactions on Dielectrics and Electrical Insulation.

[24]  Dongdong Zhao,et al.  A Study of the Effects of Stemming Strategies on Arabic Document Classification , 2019, IEEE Access.

[25]  Mahdi Hasanipanah,et al.  Novel approach for forecasting the blast-induced AOp using a hybrid fuzzy system and firefly algorithm , 2019, Engineering with Computers.

[26]  Hossam Faris,et al.  Training feedforward neural networks using multi-verse optimizer for binary classification problems , 2016, Applied Intelligence.

[27]  Farhad Pourpanah,et al.  Feature selection based on improved binary global harmony search for data classification , 2020, Appl. Soft Comput..

[28]  Hossam Faris,et al.  Binary dragonfly optimization for feature selection using time-varying transfer functions , 2018, Knowl. Based Syst..

[29]  Hossam Faris,et al.  An efficient binary Salp Swarm Algorithm with crossover scheme for feature selection problems , 2018, Knowl. Based Syst..

[30]  Jin Song Dong,et al.  Binary Harris Hawks Optimizer for High-Dimensional, Low Sample Size Feature Selection , 2019, Algorithms for Intelligent Systems.

[31]  Guanglu Sun,et al.  Feature selection for IoT based on maximal information coefficient , 2018, Future Gener. Comput. Syst..

[32]  Deepak Gupta,et al.  Optimal Feature Selection-Based Medical Image Classification Using Deep Learning Model in Internet of Medical Things , 2020, IEEE Access.

[33]  Slawomir Koziel,et al.  Fast tolerance-aware design optimization of miniaturized microstrip couplers using variable-fidelity EM simulations and response features , 2019, Engineering Computations.

[34]  Souad Larabi Marie-Sainte,et al.  Firefly Algorithm based Feature Selection for Arabic Text Classification , 2020, J. King Saud Univ. Comput. Inf. Sci..

[35]  Sunanda Das,et al.  Ensemble feature selection using bi-objective genetic algorithm , 2017, Knowl. Based Syst..

[36]  Mohammed Azmi Al-Betar,et al.  Gene selection for cancer classification by combining minimum redundancy maximum relevancy and bat-inspired algorithm , 2017, Int. J. Data Min. Bioinform..

[37]  Laith Mohammad Abualigah,et al.  Feature Selection and Enhanced Krill Herd Algorithm for Text Document Clustering , 2018, Studies in Computational Intelligence.

[38]  Amir H. Gandomi,et al.  The Arithmetic Optimization Algorithm , 2021, Computer Methods in Applied Mechanics and Engineering.

[39]  Seyedali Mirjalili,et al.  A Review of Grey Wolf Optimizer-Based Feature Selection Methods for Classification , 2019, Algorithms for Intelligent Systems.

[40]  Sankalap Arora,et al.  Binary butterfly optimization approaches for feature selection , 2019, Expert Syst. Appl..

[41]  Eid Emary,et al.  Feature selection approach based on moth-flame optimization algorithm , 2016, 2016 IEEE Congress on Evolutionary Computation (CEC).

[42]  Hiroshi Motoda,et al.  Feature Selection for Knowledge Discovery and Data Mining , 1998, The Springer International Series in Engineering and Computer Science.

[43]  Mohammed A. Awadallah,et al.  An improved Dragonfly Algorithm for feature selection , 2020, Knowl. Based Syst..

[44]  Philip Kollmannsberger,et al.  Efficient Classification of White Blood Cell Leukemia with Improved Swarm Optimization of Deep Features , 2020, Scientific Reports.

[45]  Aboul Ella Hassanien,et al.  Binary grey wolf optimization approaches for feature selection , 2016, Neurocomputing.

[46]  Diego Oliva,et al.  Image segmentation via multilevel thresholding using hybrid optimization algorithms , 2018, J. Electronic Imaging.

[47]  Z. Algamal,et al.  A robust quantitative structure–activity relationship modelling of influenza neuraminidase a/PR/8/34 (H1N1) inhibitors based on the rank-bridge estimator , 2019, SAR and QSAR in environmental research.

[48]  Seyed Mohammad Mirjalili,et al.  Improved Salp Swarm Algorithm based on opposition based learning and novel local search algorithm for feature selection , 2020, Expert Syst. Appl..

[49]  Laith Abualigah,et al.  Group search optimizer: a nature-inspired meta-heuristic optimization algorithm with its results, variants, and applications , 2020, Neural Computing and Applications.

[50]  Seyed Mohammad Mirjalili,et al.  Whale optimization approaches for wrapper feature selection , 2018, Appl. Soft Comput..

[51]  Ravi Shankar,et al.  A Firefly Algorithm Based Wrapper-Penalty Feature Selection Method for Cancer Diagnosis , 2018, ICCSA.

[52]  D. Nayak,et al.  Comparative analysis, distribution, and characterization of microsatellites in Orf virus genome , 2020, Scientific Reports.

[53]  Hossam Faris,et al.  Evolutionary Population Dynamics and Grasshopper Optimization approaches for feature selection problems , 2017, Knowl. Based Syst..

[54]  Hossam Faris,et al.  An enhanced associative learning-based exploratory whale optimizer for global optimization , 2019, Neural Computing and Applications.

[55]  Xin-She Yang,et al.  Nature-Inspired Metaheuristic Algorithms , 2008 .

[56]  Zakariya Yahya Algamal,et al.  A new hybrid firefly algorithm and particle swarm optimization for tuning parameter estimation in penalized support vector machine with application in chemometrics , 2019, Chemometrics and Intelligent Laboratory Systems.

[57]  Hossam Faris,et al.  Efficient Hybrid Nature-Inspired Binary Optimizers for Feature Selection , 2019, Cognitive Computation.

[58]  K. Muneeswaran,et al.  Firefly algorithm based feature selection for network intrusion detection , 2019, Comput. Secur..

[59]  Songfeng Lu,et al.  Feature Selection Based on Improved Runner-Root Algorithm Using Chaotic Singer Map and Opposition-Based Learning , 2017, ICONIP.

[60]  M. Abd Elaziz,et al.  Improved ANFIS model for Forecasting Wuhan City Air Quality and Analysis COVID-19 Lockdown Impacts on Air Quality. , 2020, Environmental research.

[61]  Raed M. Shubair,et al.  Indoor Localization for IoT Using Adaptive Feature Selection: A Cascaded Machine Learning Approach , 2019, IEEE Antennas and Wireless Propagation Letters.

[62]  Gh. S. El-tawel,et al.  Improved salp swarm algorithm for feature selection , 2020, J. King Saud Univ. Comput. Inf. Sci..

[63]  Mita Nasipuri,et al.  A GA based hierarchical feature selection approach for handwritten word recognition , 2019, Neural Computing and Applications.

[64]  Pengfei Duan,et al.  A Hybrid Method of Sine Cosine Algorithm and Differential Evolution for Feature Selection , 2017, ICONIP.

[65]  Nabil Neggaz,et al.  Boosting salp swarm algorithm by sine cosine algorithm and disrupt operator for feature selection , 2020, Expert Syst. Appl..