KPLS Optimization With Nature-Inspired Metaheuristic Algorithms

Mello-Román, Jorge Daniel; Hernández, Adolfo

doi:10.1109/access.2020.3019771

Cited by 5 publications

(9 citation statements)

References 56 publications

(85 reference statements)

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“…To maximize predictive ability, there is need to focus on metaheuristic optimization algorithm that will address many problems in modelling on feature selection and text classification. For instance, using Kernel partial least squares regression (KPLS) technique could optimize predictive ability [154]. x.…”

Section: Other Issues and Possible Solutionsmentioning

confidence: 99%

A systematic review of emerging feature selection optimization methods for optimal text classification: the present state and prospective opportunities

Omolara

Abiodun

Alawida

et al. 2021

Neural Comput & Applic

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Specialized data preparation techniques, ranging from data cleaning, outlier detection, missing value imputation, feature selection (FS), amongst others, are procedures required to get the most out of data and, consequently, get the optimal performance of predictive models for classification tasks. FS is a vital and indispensable technique that enables the model to perform faster, eliminate noisy data, remove redundancy, reduce overfitting, improve precision and increase generalization on testing data. While conventional FS techniques have been leveraged for classification tasks in the past few decades, they fail to optimally reduce the high dimensionality of the feature space of texts, thus breeding inefficient predictive models. Emerging technologies such as the metaheuristics and hyper-heuristics optimization methods provide a new paradigm for FS due to their efficiency in improving the accuracy of classification, computational demands, storage, as well as functioning seamlessly in solving complex optimization problems with less time. However, little details are known on best practices for case-to-case usage of emerging FS methods. The literature continues to be engulfed with clear and unclear findings in leveraging effective methods, which, if not performed accurately, alters precision, real-world-use feasibility, and the predictive model's overall performance. This paper reviews the present state of FS with respect to metaheuristics and hyper-heuristic methods. Through a systematic literature review of over 200 articles, we set out the most recent findings and trends to enlighten analysts, practitioners and researchers in the field of data analytics seeking clarity in understanding and implementing effective FS optimization methods for improved text classification tasks.

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Section: Other Issues and Possible Solutionsmentioning

confidence: 99%

A systematic review of emerging feature selection optimization methods for optimal text classification: the present state and prospective opportunities

Omolara

Abiodun

Alawida

et al. 2021

Neural Comput & Applic

View full text Add to dashboard Cite

show abstract

“…To solve the optimization problem, an algorithm is implemented in a general iterative optimizer, in this case MA [15]. The problem is solved exclusively for feasible solutions of θ and h, infeasible solutions are discarded [44].…”

Section: Methodsmentioning

confidence: 99%

“…The work of Fu et al [13] presents a methodology for simultaneous estimation of the kernel function parameter and the number of components in KPCA and KPLS models. The aim of this paper is to propose a methodology to improve the predictive ability of the KPLS regression taking as reference the approach proposed by Mello-Román and Hernández [14,15] of metaheuristic tuning of the KPLS regression but using as iterative optimizer the Memetic algorithms (MA) in the selection of the number of components and the kernel function parameter.…”

Section: Introductionmentioning

confidence: 99%

“…Nature-inspired metaheuristic algorithms such as GA, PSO and others have demonstrated good performance in optimising KPLS regression, however, they require significant computational effort that increases significantly with increasing iterations [14,15]. In this article, MA is chosen as a KPLS regression optimizer because of its ability to generate optimal solutions in a reasonable time, even for very complex problem structures [16].…”

Section: Introductionmentioning

confidence: 99%

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Improved Predictive Ability of KPLS Regression with Memetic Algorithms

2021

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Kernel partial least squares regression (KPLS) is a non-linear method for predicting one or more dependent variables from a set of predictors, which transforms the original datasets into a feature space where it is possible to generate a linear model and extract orthogonal factors also called components. A difficulty in implementing KPLS regression is determining the number of components and the kernel function parameters that maximize its performance. In this work, a method is proposed to improve the predictive ability of the KPLS regression by means of memetic algorithms. A metaheuristic tuning procedure is carried out to select the number of components and the kernel function parameters that maximize the cumulative predictive squared correlation coefficient, an overall indicator of the predictive ability of KPLS. The proposed methodology led to estimate optimal parameters of the KPLS regression for the improvement of its predictive ability.

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“…Chaotic GWO was presented to increase the convergence speed of GWO [47]. Also, researchers have used many different technics to improve GWO [48]- [51].…”

Section: Introductionmentioning

confidence: 99%

A new K-means grey wolf algorithm for engineering problems

Mohammed¹,

Abdul²,

Rashid

et al. 2021

WJE

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Purpose This paper aims at studying meta-heuristic algorithms. One of the common meta-heuristic optimization algorithms is called grey wolf optimization (GWO). The key aim is to enhance the limitations of the wolves’ searching process of attacking gray wolves. Design/methodology/approach The development of meta-heuristic algorithms has increased by researchers to use them extensively in the field of business, science and engineering. In this paper, the K-means clustering algorithm is used to enhance the performance of the original GWO; the new algorithm is called K-means clustering gray wolf optimization (KMGWO). Findings Results illustrate the efficiency of KMGWO against to the GWO. To evaluate the performance of the KMGWO, KMGWO applied to solve CEC2019 benchmark test functions. Originality/value Results prove that KMGWO is superior to GWO. KMGWO is also compared to cat swarm optimization (CSO), whale optimization algorithm-bat algorithm (WOA-BAT), WOA and GWO so KMGWO achieved the first rank in terms of performance. In addition, the KMGWO is used to solve a classical engineering problem and it is superior.

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KPLS Optimization With Nature-Inspired Metaheuristic Algorithms

Cited by 5 publications

References 56 publications

A systematic review of emerging feature selection optimization methods for optimal text classification: the present state and prospective opportunities

A systematic review of emerging feature selection optimization methods for optimal text classification: the present state and prospective opportunities

Improved Predictive Ability of KPLS Regression with Memetic Algorithms

A new K-means grey wolf algorithm for engineering problems

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