Feature Selection Using an Improved Gravitational Search Algorithm

Feature selection is an important issue in the field of machine learning, which can reduce misleading computations and improve classification performance. Generally, feature selection can be considered as a binary optimization problem. Gravitational Search Algorithm (GSA) is a population-based heuristic algorithm inspired by Newton’s laws of gravity and motion. Although GSA shows good performance in solving optimization problems, it has a shortcoming of premature convergence. In this paper, the concept of global memory is introduced and the definition of exponential $Kbest$ is used in an improved version of GSA called IGSA. In this algorithm, the position of the optimal solution obtained so far is memorized, which can effectively prevent particles from gathering together and moving slowly. In this way, the exploitation ability of the algorithm gets improved, and a proper balance between exploration and exploitation gets established. Besides, the exponential $Kbest$ can significantly decrease the running time. In order to solve feature selection problem, a binary IGSA (BIGSA) is further introduced. The proposed algorithm is tested on a set of standard datasets and compared with other algorithms. The experimental results confirm the high efficiency of BIGSA for feature selection.

[1]  Witold Pedrycz,et al.  Variational Inference-Based Automatic Relevance Determination Kernel for Embedded Feature Selection of Noisy Industrial Data , 2019, IEEE Transactions on Industrial Electronics.

[2]  Lei Zhu,et al.  A Modified Gravitational Search Algorithm for Function Optimization , 2019, IEEE Access.

[3]  Sushama Nagpal,et al.  Feature Selection using Gravitational Search Algorithm for Biomedical Data , 2017 .

[4]  Raju Pal,et al.  Chaotic Kbest gravitational search algorithm (CKGSA) , 2016, 2016 Ninth International Conference on Contemporary Computing (IC3).

[5]  Bijaya K. Panigrahi,et al.  Binary grey wolf optimizer models for profit based unit commitment of price-taking GENCO in electricity market , 2019, Swarm Evol. Comput..

[6]  Ali Mahani,et al.  Gravitational search algorithm with both attractive and repulsive forces , 2017, Soft Computing.

[7]  Pendar Alirezazadeh,et al.  A Genetic Algorithm-Based Feature Selection for Kinship Verification , 2015, IEEE Signal Processing Letters.

[8]  Zhongfei Zhang,et al.  Multimedia Retrieval via Deep Learning to Rank , 2015, IEEE Signal Processing Letters.

[9]  Meng Luo,et al.  Compound feature selection and parameter optimization of ELM for fault diagnosis of rolling element bearings. , 2016, ISA transactions.

[10]  Mansour Sheikhan,et al.  Hybrid of binary gravitational search algorithm and mutual information for feature selection in intrusion detection systems , 2015, Soft Computing.

[11]  Xiaoyan Xiong,et al.  Feature subset selection by gravitational search algorithm optimization , 2014, Inf. Sci..

[12]  Andrew Lewis,et al.  Adaptive gbest-guided gravitational search algorithm , 2014, Neural Computing and Applications.

[13]  Himanshu Mittal,et al.  An optimum multi-level image thresholding segmentation using non-local means 2D histogram and exponential Kbest gravitational search algorithm , 2018, Eng. Appl. Artif. Intell..

[14]  Zhiqiang Ge,et al.  Data Mining and Analytics in the Process Industry: The Role of Machine Learning , 2017, IEEE Access.

[15]  Lei Ma,et al.  A Novel Wrapper Approach for Feature Selection in Object-Based Image Classification Using Polygon-Based Cross-Validation , 2017, IEEE Geoscience and Remote Sensing Letters.

[16]  Xiaoyan Xiong,et al.  A novel hybrid system for feature selection based on an improved gravitational search algorithm and k-NN method , 2015, Appl. Soft Comput..

[17]  F. Jakab,et al.  Text categorization with machine learning and hierarchical structures , 2015, 2015 13th International Conference on Emerging eLearning Technologies and Applications (ICETA).

[18]  Amin Alizadeh Naeini,et al.  Particle Swarm Optimization for Object-Based Feature Selection of VHSR Satellite Images , 2018, IEEE Geoscience and Remote Sensing Letters.

[19]  Rajesh Kumar,et al.  Binary whale optimization algorithm: a new metaheuristic approach for profit-based unit commitment problems in competitive electricity markets , 2019 .

[20]  Zhixin Sun,et al.  An Improved Feature Selection Algorithm Based on Ant Colony Optimization , 2018, IEEE Access.

[21]  Leandro dos Santos Coelho,et al.  Binary optimization using hybrid particle swarm optimization and gravitational search algorithm , 2014, Neural Computing and Applications.

[22]  Hossein Nezamabadi-pour,et al.  GSA: A Gravitational Search Algorithm , 2009, Inf. Sci..

[23]  Yuefei Zhu,et al.  A Deep Learning Approach for Intrusion Detection Using Recurrent Neural Networks , 2017, IEEE Access.

[24]  Hossein Nezamabadi-pour,et al.  BGSA: binary gravitational search algorithm , 2010, Natural Computing.

[25]  Stéphane Espié,et al.  Powered Two-Wheeler Riding Pattern Recognition Using a Machine-Learning Framework , 2015, IEEE Transactions on Intelligent Transportation Systems.

[26]  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.

[27]  Lorenzo Bruzzone,et al.  Feature Selection Based on High Dimensional Model Representation for Hyperspectral Images , 2017, IEEE Transactions on Image Processing.

[28]  Feng Zhu,et al.  A Compound Structure for Wind Speed Forecasting Using MKLSSVM with Feature Selection and Parameter Optimization , 2018, Mathematical Problems in Engineering.

[29]  Xiang Liao,et al.  Study on unit commitment problem considering pumped storage and renewable energy via a novel binary artificial sheep algorithm , 2017 .