Hybrid Binary Bat Algorithm with Cross-Entropy Method for Feature Selection

Li, Guocheng; Le, Chengyi

doi:10.1109/iccre.2019.8724270

Cited by 8 publications

(3 citation statements)

References 31 publications

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“…The binary search space can be considered a hypercube. The algorithm's artificial search agents return different bit numbers to move to corners closer or farther from this hypercube [23]. The BA returns the velocity values in real space and the position update is done using Eq.…”

Section: Binary Bat Algorithm (Bba)mentioning

confidence: 99%

Learning Machine Based on Optimized Dimensionality Reduction Algorithm for Fault Diagnosis of Rotor Broken Bars in Induction Machine

Fares¹,

Zoubir²,

Thlaijia³

et al. 2022

EJEE

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Induction machine health monitoring is considered a developing technology for the online detection of faults that occur even at the initial stage. The objective of this study is to present an artificial intelligence (AI) technique for the detection and localization of adjacent and distant broken bar faults in the induction machine, through a multi-winding model for the simulation of these cases. In this work, it was found that the application of Artificial Neural Networks (ANN) based on Mean Squared Error (MSE) and Random Forest (decision tree) plays an important role in detecting and locating defaults. The stator current signal Ias of a motor in the dynamic state was acquired from a healthy and faulty motor with a broken rotor bar fault. 9 statistical features and 8 wavelet packet parameters are extracted from the stator current signal. These features were employed as an input vector to train and test the ANN and random fores29t and determine whether the motor was running under normal conditions or defective. For optimizing the rotor bar defect classification procedure, feature selection algorithms are adopted, such as BBAT and BPSO. For feature reduction, we used the principal component analysis (PCA) algorithm, to reduce the number of features. The results showed that the random forest classifier based on statistical parameters and wavelet packet parameters followed by PCA can detect the defective with high accuracy (98.3333%) compared to other methods.

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Section: Binary Bat Algorithm (Bba)mentioning

confidence: 99%

Learning Machine Based on Optimized Dimensionality Reduction Algorithm for Fault Diagnosis of Rotor Broken Bars in Induction Machine

Fares¹,

Zoubir²,

Thlaijia³

et al. 2022

EJEE

View full text Add to dashboard Cite

show abstract

“…In order to solve problems in discrete space, the copy of the bat algorithm has been updated to a binary version that deals with problems on a vector basis [0,1] where molecules can travel to all corners of any excessive material using only 0 or 1 values [12]. This means that the bat algorithm (BBA) is somewhat similar to the basic algorithm (BA), but depending on the search space where the BA operates at continuous distances while BBA search is based on the discrete space by 0 and 1 this process requires a function that changes values between 0 and 1 and is called the transfer function [13].…”

Section: Bat Algorithmmentioning

confidence: 99%

Classification of diseases using a hybrid fuzzy mutual information technique with binary bat algorithm

AL-Taie¹,

Qasim²

2020

Ann Proteom Bioinform

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Genetic datasets have a large number of features that may signifi cantly affect the disease classifi cation process, especially datasets related to cancer diseases. Evolutionary algorithms (EA) are used to fi nd the fastest and best way to perform these calculations, such as the bat algorithm (BA) by reducing the dimensions of the search area after changing it from continuous to discrete. In this paper, a method of gene selection was proposed two sequent stages: in the fi rst stage, the fuzzy mutual information (FMI) method is used to choose the most important genes selected through a fuzzy model that was built based on the dataset size. In the second stage, the BBA is used to reduce and determine a fi xed number of genes affecting the process of classifi cation, which came from the fi rst stage. The proposed algorithm, FMI_BBA, describes effi ciency, by obtaining a higher classifi cation accuracy and a few numbers of selected genes compared to other algorithms.How to cite this article: Yonis AL-Taie FA, Qasim OS. Classifi cation of diseases using a hybrid fuzzy mutual information technique with binary bat algorithm. Ann Proteom Bioinform. 2020; 4: 001-005.

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“…The classification performance is improved using BA in the large data [6]. In [7] a hybrid Cross-Entropy method (BBACE) with Binary Bat Algorithm is used for feature selection in Big Data. To evaluate the effectiveness of the proposed method, the classifier such as Support Vector Machine and Naïve Bayes is used as a comparison regarding to time processing and accuracy.…”

Section: Introductionmentioning

confidence: 99%

A New Discretization Approach of Bat and K-Means

Mohamed¹,

Samsudin²

2021

IJACSA

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Bat algorithm is one of the optimization techniques that mimic the behavior of bat. Bat algorithm is a powerful algorithm in finding the optimum feature data collection. Classification is one of the data mining tasks that useful in knowledge representation. But, the high dimensional data become the issue in the classification that interrupt classification accuracy. From the literature, feature selection and discretization able to overcome the problem. Therefore, this study aims to show Bat algorithm is potential as a discretization approach and as a feature selection to improve classification accuracy. In this paper, a new hybrid Bat-K-Mean algorithm refer as hBA is proposed to convert continuous data into discrete data called as optimize discrete dataset. Then, Bat is used as feature selection to select the optimum feature from the optimized discrete dataset in order to reduce the dimension of data. The experiment is conducted by using k-Nearest Neighbor to evaluate the effectiveness of discretization and feature selection in classification by comparing with continuous dataset without feature selection, discrete dataset without feature selection, and continuous dataset without discretization and feature selection. Also, to show Bat is potential as a discretization approach and feature selection method. . The experiments were carried out using a number of benchmark datasets from the UCI machine learning repository. The results show the classification accuracy is improved with the Bat-K-Means optimized discretization and Bat optimized feature selection.

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Hybrid Binary Bat Algorithm with Cross-Entropy Method for Feature Selection

Cited by 8 publications

References 31 publications

Learning Machine Based on Optimized Dimensionality Reduction Algorithm for Fault Diagnosis of Rotor Broken Bars in Induction Machine

Learning Machine Based on Optimized Dimensionality Reduction Algorithm for Fault Diagnosis of Rotor Broken Bars in Induction Machine

Classification of diseases using a hybrid fuzzy mutual information technique with binary bat algorithm

A New Discretization Approach of Bat and K-Means

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