Bat algorithm for variable selection in multivariate classification modeling using linear discriminant analysis

Souza, Juliana da Cruz; Soares, Sófacles Figueredo Carreiro; Paula, Lauro Cássio Martins de; Coelho, Clarimar J.; Araújo, Mário César Ugulino de; Silva, Edvan Cirino da

doi:10.1016/j.microc.2022.108382

Cited by 11 publications

(5 citation statements)

References 39 publications

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“…Tujuan dari ekstraski fitur adalah mempertahankan sebagian besar informasi yang relevan dengan melakukan kompresi. Beberapa teknik dari ekstraksi fitur adalah Principal Component Analysis (PCA) [15]- [18], Linear Discriminant Analysis (LDA) [19]- [22], dan Fast Fourier Transform (FFT) [23]- [27].…”

Section: Pendahuluanunclassified

Ekstraksi Fitur pada File Gambar dengan Metode Fast Fourier Transform

Prasetiyowati

2024

INFORMAL

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There are several methods for performing feature extraction, one of which is the Fast Fourier Transform (FFT). FFT is an effective way to convert signals or time series data to the frequency domain. This study uses the FFT method for image data. Image data is transformed using FFT and returned using the Inverse Fast Fourier Transform (IFFT). From the results of the feature extraction test on nine image files, it was found that 78% of the image data experienced a decrease in size, 11% of the images had a fixed size, and 11% of the image data experienced an increase in size.

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Section: Pendahuluanunclassified

Ekstraksi Fitur pada File Gambar dengan Metode Fast Fourier Transform

Prasetiyowati

2024

INFORMAL

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“…The algorithm simulates the behavior of bats as they search for prey in the dark, using echolocation and adjusting their flight paths based on feedback from their environment. The BA involves moving "bats" through the solution space by iteratively altering their Souza et al [15] presents the BA-LDA algorithm, a bat-inspired technique created to replace linear discriminant analysis (LDA) in multivariate classification for variable selection. This approach was examined and contrasted with the genetic algorithm (GA-LDA) and successive projection algorithm (SPA-LDA), taking inspiration from the echolocation behavior of bats during prey search.…”

Section: Bat Algorithmmentioning

confidence: 99%

“…If the optimization problem involves discrete variables (e.g., integer variables), the BA is adapted to handle these discrete values. One approach is to introduce a discretization step to map the continuous search space of the BA to a discrete space that corresponds to a problem [14], [15]. This can involve rounding or mapping continuous parameter values to the nearest discrete values.…”

Section: Bat Algorithmmentioning

confidence: 99%

An improve unsupervised discretization using optimization algorithms for classification problems

Mohamed,

Samsudin

2024

IJEECS

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This paper addresses the classification problem in machine learning, focusing on predicting class labels for datasets with continuous features. Recognizing the critical role of discretization in enhancing classification performance, the study integrates equal width binning (EWB) with two optimization algorithms: the bat algorithm (BA), referred to as EB, and the whale optimization algorithm (WOA), denoted as EW. The primary objective is to determine the optimal technique for predicting relevant class labels. The paper emphasizes the significance of discretization in data preprocessing, offering a comprehensive approach that combines discretization techniques with optimization algorithms. An investigative study was undertaken to assess the efficiency of EB and EW by evaluating their classification performance using Naive Bayes and K-nearest neighbor algorithms on four continuous datasets sourced from the UCI datasets. According to the experimental findings, the suggested EB has a major effect on the accuracy, recall, and F-measure of data classification. The classification performance using EB outperforms other existing approaches for all datasets.

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“…This work used three discrete optimization methods for the master stage: a parallel version of the Montecarlo method (PMC) and two parallel-discrete versions of traditional continuous optimization methods, i.e., the genetic and crow search algorithms. The selection of these methods was based on the excellent results reported in the literature with regard to the solution of similar electrical engineering problems [18,29,32,33]. This subsection outlines the iterative process of each of these algorithms.…”

Section: Master Stagementioning

confidence: 99%

Optimal Integration of Battery Systems in Grid-Connected Networks for Reducing Energy Losses and CO2 Emissions

2023

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This work addressed the problem regarding the optimal integration of battery systems (BS) in grid-connected networks (GCNs) with the purpose of reducing energy losses and CO2 emissions, for which it formulates a mathematical model that considers the constraints associated with the operation of GCNs in a distributed generation environment that includes BS and variable power generation related to photovoltaic (PV) distributed generation (DG) and demand. As solution strategies, three different master–slave methodologies are employed that are based on sequential programming methods, with the aim to avoid the implementation of commercial software. In the master stage, to solve the problem regarding the location and the type of batteries to be used, parallel-discrete versions of the Montecarlo method (PMC), a genetic algorithm (PDGA), and the search crow algorithm (PDSCA) are employed. In the slave stage, the particle swarm optimization algortihm (PSO) is employed to solve the problem pertaining to the operation of the batteries, using a matrix hourly power flow to assess the impact of each possible solution proposed by the master–slave methodologies on the objective functions and constraints. As a test scenario, a GCN based on the 33-bus test systems is used, which considers the generation, power demand, and CO2 emissions behavior of the city of Medellín (Colombia). Each algorithm is executed 1000 times, with the aim to evaluate the effectiveness of each solution in terms of its quality, standard deviation, and processing times. The simulation results obtained in this work demostrate that PMC/PSO is the master–slave methodology with the best performance in terms of solution quality, repeatability, and processing time.

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Bat algorithm for variable selection in multivariate classification modeling using linear discriminant analysis

Cited by 11 publications

References 39 publications

Ekstraksi Fitur pada File Gambar dengan Metode Fast Fourier Transform

Ekstraksi Fitur pada File Gambar dengan Metode Fast Fourier Transform

An improve unsupervised discretization using optimization algorithms for classification problems

Optimal Integration of Battery Systems in Grid-Connected Networks for Reducing Energy Losses and CO2 Emissions

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