An Enhanced Evolutionary Based Feature Selection Approach Using Grey Wolf Optimizer for the Classification of High-dimensional Biological Data (original) (raw)
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An Improved Binary Grey-Wolf Optimizer with Simulated Annealing for Feature Selection
IEEE Access
This paper proposes improvements to the binary grey-wolf optimizer (BGWO) to solve the feature selection (FS) problem associated with high data dimensionality, irrelevant, noisy, and redundant data that will then allow machine learning algorithms to attain better classification/clustering accuracy in less training time. We propose three variants of BGWO in addition to the standard variant, applying different transfer functions to tackle the FS problem. Because BGWO generates continuous values and FS needs discrete values, a number of V-shaped, S-shaped, and U-shaped transfer functions were investigated for incorporation with BGWO to convert their continuous values to binary. After investigation, we note that the performance of BGWO is affected by the selection of the transfer function. Then, in the first variant, we look to reduce the local minima problem by integrating an exploration capability to update the position of the grey wolf randomly within the search space with a certain probability; this variant was abbreviated as IBGWO. Consequently, a novel mutation strategy is proposed to select a number of the worst grey wolves in the population which are updated toward the best solution and randomly within the search space based on a certain probability to determine if the update is either toward the best or randomly. The number of the worst grey wolf selected by this strategy is linearly increased with the iteration. Finally, this strategy is combined with IBGWO to produce the second variant of BGWO that was abbreviated as LIBGWO. In the last variant, simulated annealing (SA) was integrated with LIBGWO to search around the best-so-far solution at the end of each iteration in order to identify better solutions. The performance of the proposed variants was validated on 32 datasets taken from the UCI repository and compared with six wrapper feature selection methods. The experiments show the superiority of the proposed improved variants in producing better classification accuracy than the other selected wrapper feature selection algorithms. INDEX TERMS Grey-wolf optimizer, feature selection, simulated annealing, mutation strategy.
Hybrid Gradient Descent Grey Wolf Optimizer for Optimal Feature Selection
BioMed Research International, 2021
Feature selection is the process of decreasing the number of features in a dataset by removing redundant, irrelevant, and randomly class-corrected data features. By applying feature selection on large and highly dimensional datasets, the redundant features are removed, reducing the complexity of the data and reducing training time. The objective of this paper was to design an optimizer that combines the well-known metaheuristic population-based optimizer, the grey wolf algorithm, and the gradient descent algorithm and test it for applications in feature selection problems. The proposed algorithm was first compared against the original grey wolf algorithm in 23 continuous test functions. The proposed optimizer was altered for feature selection, and 3 binary implementations were developed with final implementation compared against the two implementations of the binary grey wolf optimizer and binary grey wolf particle swarm optimizer on 6 medical datasets from the UCI machine learning ...
An Excited Binary Grey Wolf Optimizer for Feature Selection in Highly Dimensional Datasets
2020
Currently, feature selection is an important but challenging task in both data mining and machine learning, especially when handling highly dimensioned datasets with noisy, redundant and irrelevant attributes. These datasets are characterized by many attributes with limited sample-sizes, making classification models overfit. Thus, there is a dire need to develop efficient feature selection techniques to aid in deriving an optimal informative subset of features from these datasets prior to classification. Although grey wolf optimizer (GWO) has been widely utilized in feature selection with promising results, it is normally trapped in the local optimum resulting into semi-optimal solutions. This is because its position-updated equation is good at exploitation but poor at exploration. In this paper, we propose an improved algorithm called excited binary grey wolf optimizer (EBGWO). In order to improve on exploration, a new position-updating criterion is adopted by utilizing the fitness...
ENHANCED GREY WOLF OPTIMIZER FOR MEDICAL DATASET
High dimensional data classification becomes challenging task because data are large, complex to handle, heterogeneous and hierarchical. In order to reduce the data set without affecting the classifier accuracy. The feature selection plays a vital role in large datasets and which increases the efficiency of classification to choose the important features for high dimensional classification, when those features are irrelevant or correlated. Therefore feature selection is considered to use in preprocessing before applying classifier to a data set. Thus this good choice of feature selection leads to the high classification accuracy and minimize computational cost. Though different kinds of feature selection methods are investigate for selecting and fitting features, the best algorithm should be preferred to maximize the accuracy of the classification. The proposed Hybrid kernel Improved Support Vector Machine (HISVM) classifier is used to train the parameters and optimized using Enhanced Grey wolf Optimization (EGWO). The Novel approach aimed to select minimum number of features and providing high classification accuracy.
BioMed Research International
A new master-slave binary grey wolf optimizer (MSBGWO) is introduced. A master-slave learning scheme is introduced to the grey wolf optimizer (GWO) to improve its ability to explore and get better solutions in a search space. Five high-dimensional biomedical datasets are used to test the ability of MSBGWO in feature selection. The experimental results of MSBGWO are superior in terms of classification accuracy, precision, recall, F -measure, and number of features selected when compared to those of the binary grey wolf optimizer version 2 (BGWO2), binary genetic algorithm (BGA), binary particle swarm optimization (BPSO), differential evolution (DE) algorithm, and sine-cosine algorithm (SCA).
Binary Optimization Using Hybrid Grey Wolf Optimization for Feature Selection
IEEE Access, 2019
A binary version of the hybrid grey wolf optimization (GWO) and particle swarm optimization (PSO) is proposed to solve feature selection problems in this paper. The original PSOGWO is a new hybrid optimization algorithm that benefits from the strengths of both GWO and PSO. Despite the superior performance, the original hybrid approach is appropriate for problems with a continuous search space. Feature selection, however, is a binary problem. Therefore, a binary version of hybrid PSOGWO called BGWOPSO is proposed to find the best feature subset. To find the best solutions, the wrapper-based method K-nearest neighbors classifier with Euclidean separation matric is utilized. For performance evaluation of the proposed binary algorithm, 18 standard benchmark datasets from UCI repository are employed. The results show that BGWOPSO significantly outperformed the binary GWO (BGWO), the binary PSO, the binary genetic algorithm, and the whale optimization algorithm with simulated annealing when using several performance measures including accuracy, selecting the best optimal features, and the computational time.
Computers, Materials & Continua
Selecting the most relevant subset of features from a dataset is a vital step in data mining and machine learning. Each feature in a dataset has 2 n possible subsets, making it challenging to select the optimum collection of features using typical methods. As a result, a new metaheuristicsbased feature selection method based on the dipper-throated and grey-wolf optimization (DTO-GW) algorithms has been developed in this research. Instability can result when the selection of features is subject to metaheuristics, which can lead to a wide range of results. Thus, we adopted hybrid optimization in our method of optimizing, which allowed us to better balance exploration and harvesting chores more equitably. We propose utilizing the binary DTO-GW search approach we previously devised for selecting the optimal subset of attributes. In the proposed method, the number of features selected is minimized, while classification accuracy is increased. To test the proposed method's performance against eleven other state-of-theart approaches, eight datasets from the UCI repository were used, such as binary grey wolf search (bGWO), binary hybrid grey wolf, and particle swarm optimization (bGWO-PSO), bPSO, binary stochastic fractal search (bSFS), binary whale optimization algorithm (bWOA), binary modified grey wolf optimization (bMGWO), binary multiverse optimization (bMVO), binary bowerbird optimization (bSBO), binary hysteresis optimization (bHy), and binary hysteresis optimization (bHWO). The suggested method is superior 4532 CMC, 2023, vol.74, no.2 and successful in handling the problem of feature selection, according to the results of the experiments.
Binary Multi-Objective Grey Wolf Optimizer for Feature Selection in Classification
IEEE Access
Feature selection or dimensionality reduction can be considered as a multi-objective minimization problem with two objectives: minimizing the number of features and minimizing the error rate simultaneously. Despite being a multi-objective problem, most existing approaches treat feature selection as a single-objective optimization problem. Recently, Multi-objective Grey Wolf optimizer (MOGWO) was proposed to solve multi-objective optimization problem. However, MOGWO was originally designed for continuous optimization problems and hence, it cannot be utilized directly to solve multi-objective feature selection problems which are inherently discrete in nature. Therefore, in this research, a binary version of MOGWO based on sigmoid transfer function called BMOGW-S is developed to optimize feature selection problems. A wrapper based Artificial Neural Network (ANN) is used to assess the classification performance of a subset of selected features. To validate the performance of the proposed method, 15 standard benchmark datasets from the UCI repository are employed. The proposed BMOGWO-S was compared with MOGWO with a tanh transfer function and Non-dominated Sorting Genetic Algorithm (NSGA-II) and Multi-objective Particle Swarm Optimization (MOPSO). The results showed that the proposed BMOGWO-S can effectively determine a set of non-dominated solutions. The proposed method outperforms the existing multi-objective approaches in most cases in terms of features reduction as well as classification error rate while benefiting from a lower computational cost.
Feature Selection using MultiObjective Grey Wolf Optimization Algorithm
2019
Multi Objective Grey wolf Optimization is one a meta-heuristic technique. The MOGWO has recently gained a huge research interest from numerous domains due to its impressive characteristics over other meta-heuristics optimization techniques: it has less parameters, derivation information is not required in the initial stage, scalable, flexible, easy to use. In this paper MOGWO, which is based on the leadership hunting technique of grey wolves is used for feature selection. The traditional GWO is useful for single objective optimization problems. Since, feature extraction is a multi-objective problem; this paper utilizes multiobjective GWO algorithm. In this paper, MOGWO is applied to 6 different datasets to understand its application in diverse set of problems. At first, MOGWO is used to obtain feature subsets from different datasets. Then machine learning models like KNN, random forest and logistic regression are used to obtain the accuracy results and comparison of the results is p...
Binary grey wolf optimization approaches for feature selection
Neurocomputing, 2015
In this work, a novel binary version of the grey wolf optimization (GWO) is proposed and used to select optimal feature subset for classification purposes. Grey wolf optimizer (GWO) is one of the latest bioinspired optimization techniques, which simulate the hunting process of grey wolves in nature. The binary version introduced here is performed using two different approaches. In the first approach, individual steps toward the first three best solutions are binarized and then stochastic crossover is performed among the three basic moves to find the updated binary grey wolf position. In the second approach, sigmoidal function is used to squash the continuous updated position, then stochastically threshold these values to find the updated binary grey wolf position. The two approach for binary grey wolf optimization (bGWO) are hired in the feature selection domain for finding feature subset maximizing the classification accuracy while minimizing the number of selected features. The proposed binary versions were compared to two of the common optimizers used in this domain namely particle swarm optimizer and genetic algorithms. A set of assessment indicators are used to evaluate and compared the different methods over 18 different datasets from the UCI repository. Results prove the capability of the proposed binary version of grey wolf optimization (bGWO) to search the feature space for optimal feature combinations regardless of the initialization and the used stochastic operators.