Forest optimization algorithm‐based feature selection using classifier ensemble

Moorthy, Usha; Gandhi, Usha Devi

doi:10.1111/coin.12265

Cited by 10 publications

(8 citation statements)

References 27 publications

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“…Forest trees are ranked because of their fitness function value. The tree with the highest rank is selected as the best tree (solution) and its age is set to zero to prevent its aging and extinction (Moorthy and Gandhi, 2019).…”

Section: Methodsmentioning

confidence: 99%

An energy-aware approach for resources allocating in the internet of things using a forest optimization algorithm

Zhang

Rui

2021

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Purpose Internet of things (IoT) is essential in technical, social and economic domains, but there are many challenges that researchers are continuously trying to solve. Traditional resource allocation methods in IoT focused on the optimal resource selection process, but the energy consumption for allocating resources is not considered sufficiently. This paper aims to propose a resource allocation technique aiming at energy and delay reduction in resource allocation. Because of the non-deterministic polynomial-time hard nature of the resource allocation issue and the forest optimization algorithm’s success in complex problems, the authors used this algorithm to allocate resources in IoT. Design/methodology/approach For the vast majority of IoT applications, energy-efficient communications, sustainable energy supply and reduction of latency have been major goals in resource allocation, making operating systems and applications more efficient. One of the most critical challenges in this field is efficient resource allocation. This paper has provided a new technique to solve the mentioned problem using the forest optimization algorithm. To simulate and analyze the proposed technique, the MATLAB software environment has been used. The results obtained from implementing the proposed method have been compared to the particle swarm optimization (PSO), genetic algorithm (GA) and distance-based algorithm. Findings Simulation results show that the proper performance of the proposed technique. The proposed method, in terms of “energy” and “delay,” is better than other ones (GA, PSO and distance-based algorithm). Practical implications The paper presents a useful method for improving resource allocation methods. The proposed method has higher efficiency compared to the previous methods. The MATLAB-based simulation results have indicated that energy consumption and delay have been improved compared to other algorithms, which causes the high application of this method in practical projects. In the future, the focus will be on resource failure and reducing the service level agreement violation rate concerning the number of resources. Originality/value The proposed technique can solve the mentioned problem in the IoT with the best resource utilization, low delay and reduced energy consumption. The proposed forest optimization-based algorithm is a promising technique to help enterprises participate in IoT initiatives and develop their business.

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Section: Methodsmentioning

confidence: 99%

An energy-aware approach for resources allocating in the internet of things using a forest optimization algorithm

Zhang

Rui

2021

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“…BCFA was tested on three benchmark datasets and achieved stunning results in selecting the optimal features in less time. A hybrid feature selection algorithm based on the forest optimization algorithm (FOA) and minimization of redundancy and maximum relevance (mRMR) was proposed in [48]. The results showed that applying k-NN and NB classifiers with the proposed FOA algorithm outperformed standard classifier algorithms.…”

Section: Related Workmentioning

confidence: 99%

An Efficient Heap Based Optimizer Algorithm for Feature Selection

Ali

P.²,

Elminaam³

2022

Mathematics

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The heap-based optimizer (HBO) is an innovative meta-heuristic inspired by human social behavior. In this research, binary adaptations of the heap-based optimizer B_HBO are presented and used to determine the optimal features for classifications in wrapping form. In addition, HBO balances exploration and exploitation by employing self-adaptive parameters that can adaptively search the solution domain for the optimal solution. In the feature selection domain, the presented algorithms for the binary Heap-based optimizer B_HBO are used to find feature subsets that maximize classification performance while lowering the number of selected features. The textitk-nearest neighbor (textitk-NN) classifier ensures that the selected features are significant. The new binary methods are compared to eight common optimization methods recently employed in this field, including Ant Lion Optimization (ALO), Archimedes Optimization Algorithm (AOA), Backtracking Search Algorithm (BSA), Crow Search Algorithm (CSA), Levy flight distribution (LFD), Particle Swarm Optimization (PSO), Slime Mold Algorithm (SMA), and Tree Seed Algorithm (TSA) in terms of fitness, accuracy, precision, sensitivity, F-score, the number of selected features, and statistical tests. Twenty datasets from the UCI repository are evaluated and compared using a set of evaluation indicators. The non-parametric Wilcoxon rank-sum test was used to determine whether the proposed algorithms’ results varied statistically significantly from those of the other compared methods. The comparison analysis demonstrates that B_HBO is superior or equivalent to the other algorithms used in the literature.

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“…Single variable classifiers are used for feature ranking and selection, considering the potential connection with the classifier in Reference 23. The forest optimization algorithm has been used for feature selection in collaboration with data preprocessing based upon the minimum redundancy and maximum relevance (mRMR) technique to initially remove the least important features from the feature set 24 . The t‐distributed stochastic neighbor embedding (t‐SNE) 25 maps the high‐dimensional data to lower dimensions (typically 2D or 3D) space for visualization purposes.…”

Section: Related Workmentioning

confidence: 99%

Novel principal component analysis‐based feature selection mechanism for classroom sound classification

Tsalera

Papadakis

Σαμαράκου

2021

Computational Intelligence

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Machine learning algorithms for sound classification can be supported by multiple temporal, spectral, and perceptual features extracted from the sound signal. The number of features affects the classification accuracy but also the computational resources requested, so the number of features has to be carefully selected. In this work, we propose a methodology for feature selection based on the principal component analysis. The case study has been the classification of classroom sounds during face‐to‐face module delivery and six sound types have been defined. The proposed method is applied upon a set of 143 sound features to produce feature ranking. The ranking results are compared with those provided by the Relief‐F. Then the selected features are used by five classification algorithms, Linear Discriminant Analysis (LDA), Quadratic Support Vector Machine (QSVM), k Nearest Neighbors, Boosted Trees, and Random Forest. The algorithms are executed with increasing number of features, from 1 to 143, considering both feature rankings, creating 1430 models. The performance of the classification algorithms increases rapidly with the number of features with LDA, QSVM, and Boosted Trees outperforming other methods and surpassing the accuracy ratio of 90% with 25 features.

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Forest optimization algorithm‐based feature selection using classifier ensemble

Cited by 10 publications

References 27 publications

An energy-aware approach for resources allocating in the internet of things using a forest optimization algorithm

An energy-aware approach for resources allocating in the internet of things using a forest optimization algorithm

An Efficient Heap Based Optimizer Algorithm for Feature Selection

Novel principal component analysis‐based feature selection mechanism for classroom sound classification

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