Efficient feature selection of power quality events using two dimensional (2D) particle swarms

Hafiz, Faizal; Swain, Akshya; Naik, Chirag A.; Patel, Nitish

doi:10.1016/j.asoc.2019.105498

Cited by 15 publications

(9 citation statements)

References 59 publications

(157 reference statements)

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“…Focusing part of the search process on parsimonious solutions is therefore likely to improve the search performance. This is further corroborated by the results of our earlier investigations in (Hafiz et al, 2018b(Hafiz et al, , 2019b, in which the search algorithms with unitary search focus only on relevant variables (e.g., GA, BPSO and ACO) were found to be ineffectual in removing redundant variables. By introducing solution sparsity as the other search dimension, the search efforts can be directed towards efficacious and parsimonious solutions, which can effectively remove redundant variables.…”

Section: Learning Methodology Of 2dssupporting

confidence: 79%

“…This study, therefore, proposes the extension of population based search heuristic, Two-Dimensional Swarms (2DS), which explicitly focuses on the sparsity of the candidate solutions (Hafiz et al, 2018b(Hafiz et al, , 2019a(Hafiz et al, ,b, 2018a. 2DS was originally developed by the authors for the feature selection problem in (Hafiz et al, 2018b(Hafiz et al, , 2019b and subsequently extended for regressor/term selection in nonlinear system identification in (Hafiz et al, 2018a(Hafiz et al, , 2019a. In this study, we build upon 2DS framework to identify parsimonious and efficacious combination of neural architecture and feature subset, as will be discussed in the following subsections.…”

Section: Search For the Optimal Neural Architecture: Two-dimensional ...mentioning

confidence: 99%

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A Multi-criteria Approach to Evolve Sparse Neural Architectures for Stock Market Forecasting

Hafiz¹,

Broekaert²,

Torre³

et al. 2021

Preprint

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This study proposes a new framework to evolve efficacious yet parsimonious neural architectures for the movement prediction of stock market indices using technical indicators as inputs. In the light of a sparse signal-to-noise ratio under the Efficient Market hypothesis, developing machine learning methods to predict the movement of a financial market using technical indicators has shown to be a challenging problem. To this end, the neural architecture search is posed as a multi-criteria optimization problem to balance the efficacy with the complexity of architectures. In addition, the implications of different dominant trading tendencies which may be present in the pre-COVID and within-COVID time periods are investigated. An − constraint framework is proposed as a remedy to extract any concordant information underlying the possibly conflicting pre-COVID data. Further, a new search paradigm, Two-Dimensional Swarms (2DS) is proposed for the multi-criteria neural architecture search, which explicitly integrates sparsity as an additional search dimension in particle swarms. A detailed comparative evaluation of the proposed approach is carried out by considering genetic algorithm and several combinations of empirical neural design rules with a filter-based feature selection method (mRMR) as baseline approaches. The results of this study convincingly demonstrate that the proposed approach can evolve parsimonious networks with better generalization capabilities.

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Section: Learning Methodology Of 2dssupporting

confidence: 79%

Section: Search For the Optimal Neural Architecture: Two-dimensional ...mentioning

confidence: 99%

A Multi-criteria Approach to Evolve Sparse Neural Architectures for Stock Market Forecasting

Hafiz¹,

Broekaert²,

Torre³

et al. 2021

Preprint

Self Cite

View full text Add to dashboard Cite

show abstract

“…Hafiz et al [53] investigated the feature selection issues in the power quality events and proposed a two dimensional PSO feature selection method. They depended on the two dimensional in order to efficiently guide the search space of the particle swarm.…”

Section: A Feature Selectuion Methodsmentioning

confidence: 99%

“…Where these methods used several optimized algorithms such as PSO in [53] and [69], EA in [52], Ant in [58], GA in [64] and [70], CAF in [65] and [68] and deep learning in [61] and [65]. In [53] and [69] PSO algorithm was used to incorporate the features information into search space and hence selecting the most desired features and removing not required ones. In [52] EA was used to reduce the dimensionality of the search space by eliminating the unnecessary features from each iteration process, then influential features were selected at the same time.…”

Section: Sellami and Farahmentioning

confidence: 99%

A Comprehensive Review of Dimensionality Reduction Techniques for Feature Selection and Feature Extraction

Zebari¹,

Abdulazeez²,

Zeebaree³

et al. 2020

JASTT

440

210

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Due to sharp increases in data dimensions, working on every data mining or machine learning (ML) task requires more efficient techniques to get the desired results. Therefore, in recent years, researchers have proposed and developed many methods and techniques to reduce the high dimensions of data and to attain the required accuracy. To ameliorate the accuracy of learning features as well as to decrease the training time dimensionality reduction is used as a pre-processing step, which can eliminate irrelevant data, noise, and redundant features. Dimensionality reduction (DR) has been performed based on two main methods, which are feature selection (FS) and feature extraction (FE). FS is considered an important method because data is generated continuously at an ever-increasing rate; some serious dimensionality problems can be reduced with this method, such as decreasing redundancy effectively, eliminating irrelevant data, and ameliorating result comprehensibility. Moreover, FE transacts with the problem of finding the most distinctive, informative, and decreased set of features to ameliorate the efficiency of both the processing and storage of data. This paper offers a comprehensive approach to FS and FE in the scope of DR. Moreover, the details of each paper, such as used algorithms/approaches, datasets, classifiers, and achieved results are comprehensively analyzed and summarized. Besides, a systematic discussion of all of the reviewed methods to highlight authors' trends, determining the method(s) has been done, which significantly reduced computational time, and selecting the most accurate classifiers. As a result, the different types of both methods have been discussed and analyzed the findings.

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“…To understand the position update process, consider the velocity of an i th particle for a structure selection problem with N t = 5 as follows: Re-initialize the velocity of the particle 10 Set counti to zero 11 end 12 Update the velocity of the i th particle as per (3), (4) and (5) 13…”

Section: Position Updatementioning

confidence: 99%

Structure Selection of Polynomial NARX Models Using Two Dimensional (2D) Particle Swarms

Hafiz

Swain

Mendes

et al. 2018

2018 IEEE Congress on Evolutionary Computation (CEC)

Self Cite

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The present study applies a novel two-dimensional learning framework (2D-UPSO) based on particle swarms for structure selection of polynomial nonlinear auto-regressive with exogenous inputs (NARX) models. This learning approach explicitly incorporates the information about the cardinality (i.e., the number of terms) into the structure selection process. Initially, the effectiveness of the proposed approach was compared against the classical genetic algorithm (GA) based approach and it was demonstrated that the 2D-UPSO is superior. Further, since the performance of any meta-heuristic search algorithm is critically dependent on the choice of the fitness function, the efficacy of the proposed approach was investigated using two distinct information theoretic criteria such as Akaike and Bayesian information criterion. The robustness of this approach against various levels of measurement noise is also studied. Simulation results on various nonlinear systems demonstrate that the proposed algorithm could accurately determine the structure of the polynomial NARX model even under the influence of measurement noise.Index Terms-Nonlinear system identification, structure selection, NARX model, particle swarm † -'r' denotes the ratio of number of redundant terms to the total number of terms ‡ {T1, . . . T5} ∈ Tsystem denote the system term and are given in Appendix A

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Efficient feature selection of power quality events using two dimensional (2D) particle swarms

Cited by 15 publications

References 59 publications

A Multi-criteria Approach to Evolve Sparse Neural Architectures for Stock Market Forecasting

A Multi-criteria Approach to Evolve Sparse Neural Architectures for Stock Market Forecasting

A Comprehensive Review of Dimensionality Reduction Techniques for Feature Selection and Feature Extraction

Structure Selection of Polynomial NARX Models Using Two Dimensional (2D) Particle Swarms

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