scholarly journals Improved Equilibrium Optimization Algorithm Using Elite Opposition-Based Learning and New Local Search Strategy for Feature Selection in Medical Datasets

Computation ◽  
2021 ◽  
Vol 9 (6) ◽  
pp. 68
Author(s):  
Zenab Mohamed Elgamal ◽  
Norizan Mohd Yasin ◽  
Aznul Qalid Md Sabri ◽  
Rami Sihwail ◽  
Mohammad Tubishat ◽  
...  
Keyword(s):  
Machine Learning ◽  
Feature Selection ◽  
Local Search ◽  
Optimization Algorithm ◽  
Classification Accuracy ◽  
Optimization Algorithms ◽  
Search Strategies ◽  
Population Diversity ◽  
High Dimensionality ◽  
Local Optima

The rapid growth in biomedical datasets has generated high dimensionality features that negatively impact machine learning classifiers. In machine learning, feature selection (FS) is an essential process for selecting the most significant features and reducing redundant and irrelevant features. In this study, an equilibrium optimization algorithm (EOA) is used to minimize the selected features from high-dimensional medical datasets. EOA is a novel metaheuristic physics-based algorithm and newly proposed to deal with unimodal, multi-modal, and engineering problems. EOA is considered as one of the most powerful, fast, and best performing population-based optimization algorithms. However, EOA suffers from local optima and population diversity when dealing with high dimensionality features, such as in biomedical datasets. In order to overcome these limitations and adapt EOA to solve feature selection problems, a novel metaheuristic optimizer, the so-called improved equilibrium optimization algorithm (IEOA), is proposed. Two main improvements are included in the IEOA: The first improvement is applying elite opposite-based learning (EOBL) to improve population diversity. The second improvement is integrating three novel local search strategies to prevent it from becoming stuck in local optima. The local search strategies applied to enhance local search capabilities depend on three approaches: mutation search, mutation–neighborhood search, and a backup strategy. The IEOA has enhanced the population diversity, classification accuracy, and selected features, and increased the convergence speed rate. To evaluate the performance of IEOA, we conducted experiments on 21 biomedical benchmark datasets gathered from the UCI repository. Four standard metrics were used to test and evaluate IEOA’s performance: the number of selected features, classification accuracy, fitness value, and p-value statistical test. Moreover, the proposed IEOA was compared with the original EOA and other well-known optimization algorithms. Based on the experimental results, IEOA confirmed its better performance in comparison to the original EOA and the other optimization algorithms, for the majority of the used datasets.

scholarly journals A Hybrid Feature Selection Method for Improve the Accuracy of Medical Classification Process

2021 ◽  
Vol 11 (1) ◽  
pp. 50-55
Author(s):  
Maria Mohammad Yousef ◽  
Keyword(s):  
Machine Learning ◽  
Feature Selection ◽  
Dimensionality Reduction ◽  
Classification Accuracy ◽  
Fitness Function ◽  
Machine Learning Algorithms ◽  
Feature Subset Selection ◽  
High Dimensionality ◽  
Support Vector ◽  
Feature Subset

Generally, medical dataset classification has become one of the biggest problems in data mining research. Every database has a given number of features but it is observed that some of these features can be redundant and can be harmful as well as disrupt the process of classification and this problem is known as a high dimensionality problem. Dimensionality reduction in data preprocessing is critical for increasing the performance of machine learning algorithms. Besides the contribution of feature subset selection in dimensionality reduction gives a significant improvement in classification accuracy. In this paper, we proposed a new hybrid feature selection approach based on (GA assisted by KNN) to deal with issues of high dimensionality in biomedical data classification. The proposed method first applies the combination between GA and KNN for feature selection to find the optimal subset of features where the classification accuracy of the k-Nearest Neighbor (kNN) method is used as the fitness function for GA. After selecting the best-suggested subset of features, Support Vector Machine (SVM) are used as the classifiers. The proposed method experiments on five medical datasets of the UCI Machine Learning Repository. It is noted that the suggested technique performs admirably on these databases, achieving higher classification accuracy while using fewer features.

scholarly journals On the performance improvement of Butterfly Optimization approaches for global optimization and Feature Selection

PLoS ONE ◽  
2021 ◽  
Vol 16 (1) ◽  
pp. e0242612
Author(s):  
Adel Saad Assiri
Keyword(s):  
Feature Selection ◽  
Local Search ◽  
Optimization Algorithm ◽  
Grey Wolf Optimizer ◽  
Feature Selection Problem ◽  
Local Optima ◽  
Original Algorithm ◽  
Compression Spring ◽  
Whale Optimization ◽  
Speed Reducer

Butterfly Optimization Algorithm (BOA) is a recent metaheuristics algorithm that mimics the behavior of butterflies in mating and foraging. In this paper, three improved versions of BOA have been developed to prevent the original algorithm from getting trapped in local optima and have a good balance between exploration and exploitation abilities. In the first version, Opposition-Based Strategy has been embedded in BOA while in the second Chaotic Local Search has been embedded. Both strategies: Opposition-based & Chaotic Local Search have been integrated to get the most optimal/near-optimal results. The proposed versions are compared against original Butterfly Optimization Algorithm (BOA), Grey Wolf Optimizer (GWO), Moth-flame Optimization (MFO), Particle warm Optimization (PSO), Sine Cosine Algorithm (SCA), and Whale Optimization Algorithm (WOA) using CEC 2014 benchmark functions and 4 different real-world engineering problems namely: welded beam engineering design, tension/compression spring, pressure vessel design, and Speed reducer design problem. Furthermore, the proposed approches have been applied to feature selection problem using 5 UCI datasets. The results show the superiority of the third version (CLSOBBOA) in achieving the best results in terms of speed and accuracy.

scholarly journals An improved arithmetic optimization algorithm with forced switching mechanism for global optimization problems

2022 ◽  
Vol 19 (1) ◽  
pp. 473-512
Author(s):  
Rong Zheng ◽  
◽  
Heming Jia ◽  
Laith Abualigah ◽  
Qingxin Liu ◽  
...  
Keyword(s):  
Optimization Algorithm ◽  
Optimization Problems ◽  
Heuristic Method ◽  
Population Diversity ◽  
Test Functions ◽  
Design Problems ◽  
Local Optima ◽  
Switching Mechanism ◽  
Engineering Design Problems

<abstract> <p>Arithmetic optimization algorithm (AOA) is a newly proposed meta-heuristic method which is inspired by the arithmetic operators in mathematics. However, the AOA has the weaknesses of insufficient exploration capability and is likely to fall into local optima. To improve the searching quality of original AOA, this paper presents an improved AOA (IAOA) integrated with proposed forced switching mechanism (FSM). The enhanced algorithm uses the random math optimizer probability (<italic>RMOP</italic>) to increase the population diversity for better global search. And then the forced switching mechanism is introduced into the AOA to help the search agents jump out of the local optima. When the search agents cannot find better positions within a certain number of iterations, the proposed FSM will make them conduct the exploratory behavior. Thus the cases of being trapped into local optima can be avoided effectively. The proposed IAOA is extensively tested by twenty-three classical benchmark functions and ten CEC2020 test functions and compared with the AOA and other well-known optimization algorithms. The experimental results show that the proposed algorithm is superior to other comparative algorithms on most of the test functions. Furthermore, the test results of two training problems of multi-layer perceptron (MLP) and three classical engineering design problems also indicate that the proposed IAOA is highly effective when dealing with real-world problems.</p> </abstract>

An efficient algorithm for function optimization: modified stem cells algorithm

2013 ◽  
Vol 3 (1) ◽  
Author(s):  
Mohammad Taherdangkoo ◽  
Mahsa Paziresh ◽  
Mehran Yazdi ◽  
Mohammad Bagheri
Keyword(s):  
Stem Cells ◽  
Optimization Algorithm ◽  
Optimization Algorithms ◽  
Pso Algorithm ◽  
Function Optimization ◽  
Natural Behavior ◽  
Local Optima ◽  
Bee Colony ◽  
Intelligent Behavior ◽  
Improved Performance

AbstractIn this paper, we propose an optimization algorithm based on the intelligent behavior of stem cell swarms in reproduction and self-organization. Optimization algorithms, such as the Genetic Algorithm (GA), Particle Swarm Optimization (PSO) algorithm, Ant Colony Optimization (ACO) algorithm and Artificial Bee Colony (ABC) algorithm, can give solutions to linear and non-linear problems near to the optimum for many applications; however, in some case, they can suffer from becoming trapped in local optima. The Stem Cells Algorithm (SCA) is an optimization algorithm inspired by the natural behavior of stem cells in evolving themselves into new and improved cells. The SCA avoids the local optima problem successfully. In this paper, we have made small changes in the implementation of this algorithm to obtain improved performance over previous versions. Using a series of benchmark functions, we assess the performance of the proposed algorithm and compare it with that of the other aforementioned optimization algorithms. The obtained results prove the superiority of the Modified Stem Cells Algorithm (MSCA).

scholarly journals Stochastic Multiple Chaotic Local Search-Incorporated Gradient-Based Optimizer

2021 ◽  
Vol 2021 ◽  
pp. 1-16
Author(s):  
Hang Yu ◽  
Yu Zhang ◽  
Pengxing Cai ◽  
Junyan Yi ◽  
Sheng Li ◽  
...  
Keyword(s):  
Local Search ◽  
Optimization Problem ◽  
Search Strategy ◽  
State Of The Art ◽  
Population Diversity ◽  
Local Optima ◽  
Gradient Based ◽  
High Level ◽  
Search Rule ◽  
Better Than

In this study, a hybrid metaheuristic algorithm chaotic gradient-based optimizer (CGBO) is proposed. The gradient-based optimizer (GBO) is a novel metaheuristic inspired by Newton’s method which has two search strategies to ensure excellent performance. One is the gradient search rule (GSR), and the other is local escaping operation (LEO). GSR utilizes the gradient method to enhance ability of exploitation and convergence rate, and LEO employs random operators to escape the local optima. It is verified that gradient-based metaheuristic algorithms have obvious shortcomings in exploration. Meanwhile, chaotic local search (CLS) is an efficient search strategy with randomicity and ergodicity, which is usually used to improve global optimization algorithms. Accordingly, we incorporate GBO with CLS to strengthen the ability of exploration and keep high-level population diversity for original GBO. In this study, CGBO is tested with over 30 CEC2017 benchmark functions and a parameter optimization problem of the dendritic neuron model (DNM). Experimental results indicate that CGBO performs better than other state-of-the-art algorithms in terms of effectiveness and robustness.

scholarly journals SU-CCE: A Novel Feature Selection Approach for Reducing High Dimensionality

2021 ◽  
Author(s):  
A B Pawar ◽  
M A Jawale ◽  
Ravi Kumar Tirandasu ◽  
Saiprasad Potharaju
Keyword(s):  
Feature Selection ◽  
Classification Accuracy ◽  
Feature Space ◽  
Microarray Dataset ◽  
Classification Model ◽  
High Dimensionality ◽  
High Dimensional ◽  
Selection Approach ◽  
Feature Selection Approach ◽  
Careful Investigation

High dimensionality is the serious issue in the preprocessing of data mining. Having large number of features in the dataset leads to several complications for classifying an unknown instance. In a initial dataspace there may be redundant and irrelevant features present, which leads to high memory consumption, and confuse the learning model created with those properties of features. Always it is advisable to select the best features and generate the classification model for better accuracy. In this research, we proposed a novel feature selection approach and Symmetrical uncertainty and Correlation Coefficient (SU-CCE) for reducing the high dimensional feature space and increasing the classification accuracy. The experiment is performed on colon cancer microarray dataset which has 2000 features. The proposed method derived 38 best features from it. To measure the strength of proposed method, top 38 features extracted by 4 traditional filter-based methods are compared with various classifiers. After careful investigation of result, the proposed approach is competing with most of the traditional methods.

scholarly journals Simultaneous Feature Selection and Support Vector Machine Optimization Using an Enhanced Chimp Optimization Algorithm

Algorithms ◽  
2021 ◽  
Vol 14 (10) ◽  
pp. 282
Author(s):  
Di Wu ◽  
Wanying Zhang ◽  
Heming Jia ◽  
Xin Leng
Keyword(s):  
Optimization Algorithm ◽  
Classification Accuracy ◽  
Search Algorithm ◽  
Rank Correlation ◽  
Population Diversity ◽  
The Other ◽  
Support Vector ◽  
Local Optimum ◽  
Original Algorithm ◽  
Spearman’S Rank Correlation

Chimp Optimization Algorithm (ChOA), a novel meta-heuristic algorithm, has been proposed in recent years. It divides the population into four different levels for the purpose of hunting. However, there are still some defects that lead to the algorithm falling into the local optimum. To overcome these defects, an Enhanced Chimp Optimization Algorithm (EChOA) is developed in this paper. Highly Disruptive Polynomial Mutation (HDPM) is introduced to further explore the population space and increase the population diversity. Then, the Spearman’s rank correlation coefficient between the chimps with the highest fitness and the lowest fitness is calculated. In order to avoid the local optimization, the chimps with low fitness values are introduced with Beetle Antenna Search Algorithm (BAS) to obtain visual ability. Through the introduction of the above three strategies, the ability of population exploration and exploitation is enhanced. On this basis, this paper proposes an EChOA-SVM model, which can optimize parameters while selecting the features. Thus, the maximum classification accuracy can be achieved with as few features as possible. To verify the effectiveness of the proposed method, the proposed method is compared with seven common methods, including the original algorithm. Seventeen benchmark datasets from the UCI machine learning library are used to evaluate the accuracy, number of features, and fitness of these methods. Experimental results show that the classification accuracy of the proposed method is better than the other methods on most data sets, and the number of features required by the proposed method is also less than the other algorithms.

Feature selection in classification using self-adaptive owl search optimization algorithm with elitism and mutation strategies

2021 ◽  
Vol 40 (1) ◽  
pp. 535-550
Author(s):  
Ashis Kumar Mandal ◽  
Rikta Sen ◽  
Basabi Chakraborty
Keyword(s):  
Feature Selection ◽  
Optimization Algorithm ◽  
Classification Accuracy ◽  
Heuristic Algorithms ◽  
Support Vector ◽  
Svm Classifier ◽  
Binary Particle Swarm Optimization ◽  
Feature Subset ◽  
Search Optimization ◽  
Self Adaptive

The fundamental aim of feature selection is to reduce the dimensionality of data by removing irrelevant and redundant features. As finding out the best subset of features from all possible subsets is computationally expensive, especially for high dimensional data sets, meta-heuristic algorithms are often used as a promising method for addressing the task. In this paper, a variant of recent meta-heuristic approach Owl Search Optimization algorithm (OSA) has been proposed for solving the feature selection problem within a wrapper-based framework. Several strategies are incorporated with an aim to strengthen BOSA (binary version of OSA) in searching the global best solution. The meta-parameter of BOSA is initialized dynamically and then adjusted using a self-adaptive mechanism during the search process. Besides, elitism and mutation operations are combined with BOSA to control the exploitation and exploration better. This improved BOSA is named in this paper as Modified Binary Owl Search Algorithm (MBOSA). Decision Tree (DT) classifier is used for wrapper based fitness function, and the final classification performance of the selected feature subset is evaluated by Support Vector Machine (SVM) classifier. Simulation experiments are conducted on twenty well-known benchmark datasets from UCI for the evaluation of the proposed algorithm, and the results are reported based on classification accuracy, the number of selected features, and execution time. In addition, BOSA along with three common meta-heuristic algorithms Binary Bat Algorithm (BBA), Binary Particle Swarm Optimization (BPSO), and Binary Genetic Algorithm (BGA) are used for comparison. Simulation results show that the proposed approach outperforms similar methods by reducing the number of features significantly while maintaining a comparable level of classification accuracy.

Wrapper based Feature Selection using Integrative Teaching Learning Based Optimization Algorithm

2020 ◽  
Vol 17 (6) ◽  
pp. 885-894
Author(s):  
Mohan Allam ◽  
Nandhini Malaiyappan
Keyword(s):  
Feature Selection ◽  
Optimization Algorithms ◽  
Solution Space ◽  
Feature Space ◽  
Entire Solution ◽  
Training Dataset ◽  
Classification Models ◽  
Local Optima ◽  
Teaching Learning Based Optimization ◽  
Teaching Learning

The performance of the machine learning models mainly relies on the key features available in the training dataset. Feature selection is a significant job for pattern recognition for finding an important group of features to build classification models with a minimum number of features. Feature selection with optimization algorithms will improve the prediction rate of the classification models. But, tuning the controlling parameters of the optimization algorithms is a challenging task. In this paper, we present a wrapper-based model called Feature Selection with Integrative Teaching Learning Based Optimization (FS-ITLBO), which uses multiple teachers to select the optimal set of features from feature space. The goal of the proposed algorithm is to search the entire solution space without struck in the local optima of features. Moreover, the proposed method only utilizes teacher count parameter along with the size of the population and a number of iterations. Various classification models have been used for finding the fitness of instances in the population and to estimate the effectiveness of the proposed model. The robustness of the proposed algorithm has been assessed on Wisconsin Diagnostic Breast Cancer (WDBC) as well as Parkinson’s Disease datasets and compared with different wrapper-based feature selection techniques, including genetic algorithm and Binary Teaching Learning Based Optimization (BTLBO). The outcomes have confirmed that FS-ITLBO model produced the best accuracy with the optimal subset of features

An Empirical Evaluation of Feature Selection Methods

2015 ◽  
pp. 233-258 ◽  
Author(s):  
Mohsin Iqbal ◽  
Saif Ur Rehman ◽  
Saira Gillani ◽  
Sohail Asghar
Keyword(s):  
Machine Learning ◽  
Feature Selection ◽  
Classification Accuracy ◽  
Information Gain ◽  
Learning Algorithm ◽  
Empirical Evaluation ◽  
Machine Learning Algorithms ◽  
Selection Methods ◽  
The One ◽  
Processing And Storage

The key objective of the chapter would be to study the classification accuracy, using feature selection with machine learning algorithms. The dimensionality of the data is reduced by implementing Feature selection and accuracy of the learning algorithm improved. We test how an integrated feature selection could affect the accuracy of three classifiers by performing feature selection methods. The filter effects show that Information Gain (IG), Gain Ratio (GR) and Relief-f, and wrapper effect show that Bagging and Naive Bayes (NB), enabled the classifiers to give the highest escalation in classification accuracy about the average while reducing the volume of unnecessary attributes. The achieved conclusions can advise the machine learning users, which classifier and feature selection methods to use to optimize the classification accuracy, and this can be important, especially at risk-sensitive applying Machine Learning whereas in the one of the aim to reduce costs of collecting, processing and storage of unnecessary data.

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