Improved Salp Swarm Algorithm based on opposition based learning and novel local search algorithm for feature selection

Abstract Many fields such as data science, data mining suffered from the rapid growth of data volume and high data dimensionality. The main problems which are faced by these fields include the high computational cost, memory cost, and low accuracy performance. These problems will occur because these fields are mainly used machine learning classifiers. However, machine learning accuracy is affected by the noisy and irrelevant features. In addition, the computational and memory cost of the machine learning is mainly affected by the size of the used datasets. Thus, to solve these problems, feature selection can be used to select optimal subset of features and reduce the data dimensionality. Feature selection represents an important preprocessing step in many intelligent and expert systems such as intrusion detection, disease prediction, and sentiment analysis. An improved version of Salp Swarm Algorithm (ISSA) is proposed in this study to solve feature selection problems and select the optimal subset of features in wrapper-mode. Two main improvements were included into the original SSA algorithm to alleviate its drawbacks and adapt it for feature selection problems. The first improvement includes the use of Opposition Based Learning (OBL) at initialization phase of SSA to improve its population diversity in the search space. The second improvement includes the development and use of new Local Search Algorithm with SSA to improve its exploitation. To confirm and validate the performance of the proposed improved SSA (ISSA), ISSA was applied on 18 datasets from UCI repository. In addition, ISSA was compared with four well-known optimization algorithms such as Genetic Algorithm, Particle Swarm Optimization, Grasshopper Optimization Algorithm, and Ant Lion Optimizer. In these experiments four different assessment criteria were used. The rdemonstrate that ISSA outperforms all baseline algorithms in terms of fitness values, accuracy, convergence curves, and feature reduction in most of the used datasets. The wrapper feature selection mode can be used in different application areas of expert and intelligent systems and this is confirmed from the obtained results over different types of datasets.

[1]  Xiaodong Li,et al.  Iterated feature selection algorithms with layered recurrent neural network for software fault prediction , 2019, Expert Syst. Appl..

[2]  Xiaohui Huang,et al.  A feature selection approach for hyperspectral image based on modified ant lion optimizer , 2019, Knowl. Based Syst..

[3]  Azuraliza Abu Bakar,et al.  Ant colony optimization for text feature selection in sentiment analysis , 2019, Intell. Data Anal..

[4]  Ali Karsaz,et al.  A hybrid optimal PID-Fuzzy control design for seismic exited structural system against earthquake: A salp swarm algorithm , 2018, 2018 6th Iranian Joint Congress on Fuzzy and Intelligent Systems (CFIS).

[5]  Hélène Kerhervé,et al.  Two-Stage Feature Selection of Voice Parameters for Early Alzheimer's Disease Prediction , 2018, IRBM.

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

[7]  Song Liu,et al.  A Distributed Security Feature Selection Algorithm Based on K-means in Power Grid System , 2018, ICCCS.

[8]  Aboul Ella Hassanien,et al.  Fish Image Segmentation Using Salp Swarm Algorithm , 2018, AMLTA.

[9]  D. S. Guru,et al.  A Novel Feature Selection Technique for Text Classification , 2019 .

[10]  R. Vijayanand,et al.  Intrusion detection system for wireless mesh network using multiple support vector machine classifiers with genetic-algorithm-based feature selection , 2018, Comput. Secur..

[11]  Baran Hekimoglu,et al.  Parameter optimization of power system stabilizer via Salp Swarm algorithm , 2018, 2018 5th International Conference on Electrical and Electronic Engineering (ICEEE).

[12]  Yaochu Jin,et al.  Feature selection for high-dimensional classification using a competitive swarm optimizer , 2016, Soft Computing.

[13]  Mujahed Al-Dhaifallah,et al.  A Novel Robust Methodology Based Salp Swarm Algorithm for Allocation and Capacity of Renewable Distributed Generators on Distribution Grids , 2018, Energies.

[14]  Gh. S. El-tawel,et al.  Feature Selection Using Chaotic Salp Swarm Algorithm for Data Classification , 2018, Arabian Journal for Science and Engineering.

[15]  Ali Broumandnia,et al.  Image steganalysis using improved particle swarm optimization based feature selection , 2018, Applied Intelligence.

[16]  Shadi Aljawarneh,et al.  Anomaly-based intrusion detection system through feature selection analysis and building hybrid efficient model , 2017, J. Comput. Sci..

[17]  Attia A. El-Fergany,et al.  Extracting optimal parameters of PEM fuel cells using Salp Swarm Optimizer , 2018 .

[18]  Zehra Cataltepe,et al.  A Novel Feature Selection Method for the Dynamic Security Assessment of Power Systems Based on Multi-Layer Perceptrons , 2018 .

[19]  Shi-Min Hu,et al.  HFS: Hierarchical Feature Selection for Efficient Image Segmentation , 2016, ECCV.

[20]  Chenye Qiu,et al.  A novel multi-swarm particle swarm optimization for feature selection , 2019, Genetic Programming and Evolvable Machines.

[21]  Om Prakash Verma,et al.  Opposition and dimensional based modified firefly algorithm , 2016, Expert Syst. Appl..

[22]  Qiang Shen,et al.  Computational Intelligence and Feature Selection - Rough and Fuzzy Approaches , 2008, IEEE Press series on computational intelligence.

[23]  Amr Badr,et al.  A Nested Genetic Algorithm for feature selection in high-dimensional cancer Microarray datasets , 2019, Expert Syst. Appl..

[24]  Jianhong Zhou,et al.  An opposition-based learning competitive particle swarm optimizer , 2016, 2016 IEEE Congress on Evolutionary Computation (CEC).

[25]  Huan Liu,et al.  Toward integrating feature selection algorithms for classification and clustering , 2005, IEEE Transactions on Knowledge and Data Engineering.

[26]  Giancarlo Fortino,et al.  Intelligent temporal classification and fuzzy rough set-based feature selection algorithm for intrusion detection system in WSNs , 2019, Inf. Sci..

[27]  Hossam M. Zawbaa,et al.  Feature selection via Lèvy Antlion optimization , 2018, Pattern Analysis and Applications.

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

[29]  Yao Zhao,et al.  Secure Detection of Image Manipulation by Means of Random Feature Selection , 2018, IEEE Transactions on Information Forensics and Security.

[30]  Xin-She Yang,et al.  A wrapper approach for feature selection based on Bat Algorithm and Optimum-Path Forest , 2014, Expert Syst. Appl..

[31]  Mehdi Bagheri,et al.  A Novel Wind Power Forecasting Based Feature Selection and Hybrid Forecast Engine Bundled with Honey Bee Mating Optimization , 2018, 2018 IEEE International Conference on Environment and Electrical Engineering and 2018 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I&CPS Europe).

[32]  Akshi Kumar,et al.  Sentiment Analysis Using Cuckoo Search for Optimized Feature Selection on Kaggle Tweets , 2019, Int. J. Inf. Retr. Res..

[33]  Xiong Luo,et al.  Parameter Estimation for Soil Water Retention Curve Using the Salp Swarm Algorithm , 2018, Water.

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

[35]  Jin-Kao Hao,et al.  Opposition-Based Memetic Search for the Maximum Diversity Problem , 2017, IEEE Transactions on Evolutionary Computation.

[36]  Ya-Feng Liu,et al.  LLE Score: A New Filter-Based Unsupervised Feature Selection Method Based on Nonlinear Manifold Embedding and Its Application to Image Recognition , 2017, IEEE Transactions on Image Processing.

[37]  Razieh Sheikhpour,et al.  A combined Fisher and Laplacian score for feature selection in QSAR based drug design using compounds with known and unknown activities , 2018, Journal of Computer-Aided Molecular Design.

[38]  M.M.A. Salama,et al.  Opposition-Based Differential Evolution , 2008, IEEE Transactions on Evolutionary Computation.

[39]  Ignacio Rojas,et al.  Feature Selection and Assessment of Lung Cancer Sub-types by Applying Predictive Models , 2019, IWANN.

[40]  Vinod Kumar Jain,et al.  Correlation feature selection based improved-Binary Particle Swarm Optimization for gene selection and cancer classification , 2018, Appl. Soft Comput..

[41]  Mohd Ridzwan Yaakub,et al.  Metaheuristic algorithms for feature selection in sentiment analysis , 2015, 2015 Science and Information Conference (SAI).

[42]  Hadis Karimipour,et al.  Cyber intrusion detection by combined feature selection algorithm , 2019, J. Inf. Secur. Appl..

[43]  Shailendra Singh,et al.  An IWD-based feature selection method for intrusion detection system , 2017, Soft Computing.

[44]  Adel Sabry Eesa,et al.  A novel feature-selection approach based on the cuttlefish optimization algorithm for intrusion detection systems , 2015, Expert Syst. Appl..

[45]  Asit Kumar Das,et al.  Feature Selection-Based Clustering on Micro-blogging Data , 2019 .

[46]  Mita Nasipuri,et al.  A novel Harmony Search algorithm embedded with metaheuristic Opposition Based Learning , 2017, J. Intell. Fuzzy Syst..

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

[48]  N. Arunkumar,et al.  Optimized cuttlefish algorithm for diagnosis of Parkinson’s disease , 2018, Cognitive Systems Research.

[49]  David H. Wolpert,et al.  No free lunch theorems for optimization , 1997, IEEE Trans. Evol. Comput..

[50]  S. Mini,et al.  Opposition-based moth flame optimization with Cauchy mutation and evolutionary boundary constraint handling for global optimization , 2018, Soft Comput..

[51]  S. Jamali,et al.  Fault Detection in Microgrids Using Combined Classification Algorithms and Feature Selection Methods , 2019, 2019 International Conference on Protection and Automation of Power System (IPAPS).

[52]  Hossam Faris,et al.  Simultaneous Feature Selection and Support Vector Machine Optimization Using the Grasshopper Optimization Algorithm , 2018, Cognitive Computation.

[53]  Kamlesh Mistry,et al.  Feature selection using firefly optimization for classification and regression models , 2018, Decis. Support Syst..

[54]  H. Parveen Sultana,et al.  Artificial gravitational cuckoo search algorithm along with particle bee optimized associative memory neural network for feature selection in heart disease classification , 2019, Journal of Ambient Intelligence and Humanized Computing.

[55]  Xionghui Zhou,et al.  Integrating Feature Selection and Feature Extraction Methods With Deep Learning to Predict Clinical Outcome of Breast Cancer , 2018, IEEE Access.

[56]  T. Warren Liao,et al.  Artificial bee colony-based support vector machines with feature selection and parameter optimization for rule extraction , 2018, Knowledge and Information Systems.

[57]  Durairaj Devaraj,et al.  Two-Stage Hybrid Gene Selection Using Mutual Information and Genetic Algorithm for Cancer Data Classification , 2019, Journal of Medical Systems.

[58]  Aboul Ella Hassanien,et al.  Feature selection via a novel chaotic crow search algorithm , 2017, Neural Computing and Applications.

[59]  Shengwei Mei,et al.  A Two-Stage Feature Selection Method for Power System Transient Stability Status Prediction , 2019 .

[60]  Yaser Toghani Holari,et al.  Optimal Allocation of Distributed Generations and Shunt Capacitors Using Salp Swarm Algorithm , 2018, Electrical Engineering (ICEE), Iranian Conference on.

[61]  Nicolas Courty,et al.  Sparse Hilbert Schmidt Independence Criterion and Surrogate-Kernel-Based Feature Selection for Hyperspectral Image Classification , 2017, IEEE Transactions on Geoscience and Remote Sensing.

[62]  Ron Kohavi,et al.  Wrappers for Feature Subset Selection , 1997, Artif. Intell..

[63]  Arun Sharma,et al.  Diagnosis of Parkinson’s disease using modified grey wolf optimization , 2019, Cognitive Systems Research.

[64]  Dinesh Gopalani,et al.  Opposition Based Salp Swarm Algorithm for Numerical Optimization , 2018, ISDA.

[65]  Hossein Shayeghi,et al.  Application of a new hybrid forecast engine with feature selection algorithm in a power system , 2019 .

[66]  Zahid Iqbal,et al.  Detection and classification of citrus diseases in agriculture based on optimized weighted segmentation and feature selection , 2018, Comput. Electron. Agric..

[67]  Damodar Reddy Edla,et al.  RST-BatMiner: A fuzzy rule miner integrating rough set feature selection and Bat optimization for detection of diabetes disease , 2017, Appl. Soft Comput..

[68]  Mengjie Zhang,et al.  Differential evolution for filter feature selection based on information theory and feature ranking , 2018, Knowl. Based Syst..

[69]  Isabelle Guyon,et al.  An Introduction to Variable and Feature Selection , 2003, J. Mach. Learn. Res..

[70]  Ahmed A. Ewees,et al.  Improved grasshopper optimization algorithm using opposition-based learning , 2018, Expert Syst. Appl..

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

[72]  Kang Liu,et al.  Modified Bat Algorithm Based on Lévy Flight and Opposition Based Learning , 2016, Sci. Program..

[73]  Hossam Faris,et al.  Salp Swarm Algorithm: A bio-inspired optimizer for engineering design problems , 2017, Adv. Eng. Softw..

[74]  Amr Tolba,et al.  A big data approach to sentiment analysis using greedy feature selection with cat swarm optimization-based long short-term memory neural networks , 2018, The Journal of Supercomputing.

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

[76]  Ibrahim Aljarah,et al.  Improved whale optimization algorithm for feature selection in Arabic sentiment analysis , 2018, Applied Intelligence.

[77]  H. Hannah Inbarani,et al.  Hybrid Tolerance Rough Set-Firefly based supervised feature selection for MRI brain tumor image classification , 2016, Appl. Soft Comput..

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

[79]  Adiwijaya,et al.  On the Feature Selection and Classification Based on Information Gain for Document Sentiment Analysis , 2018, Appl. Comput. Intell. Soft Comput..