Comparing the Wrapper Feature Selection Evaluators on Twitter Sentiment Classification

Suchetha, N; Nikhil, Anupama; Hrudya, P.

doi:10.1109/iccids.2019.8862033

Cited by 27 publications

(24 citation statements)

References 15 publications

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“…As mentioned earlier, an accurate data-driven diagnostics scheme requires optimal feature selection as it affects the accuracy of the classifier. In a broader context, the use of filter and wrapper feature selection methods for diagnosis spans beyond hydraulic pumps to rolling bearings diagnostics/prognostics [34], gear fault detection [35], wireless sensor network intrusion [36], sentiment classification [37], etc. Wrapper methods (and global search algorithms) are efficient for discovering global solutions to variant problems while most filter methods are faced with diverse issues of instability [38]; nevertheless, as a pre-processing technique, these filter methods-Pearson's correlation, chi-square, linear discriminant analysis, etc.…”

Section: B Meta-heuristic Methods For Feature Selectionmentioning

confidence: 99%

“…, x a } and x(t) ∈L 2 (R),the wavelet transform W T (a, b) is the convolution of x(t) with a scale and conjugated wavelet ψ(t) [27]. This is shown in (10).…”

Section: ) Continuous Wavelet Transformmentioning

confidence: 99%

“…Consequently, our study aims at addressing these challenges by taking a multi-domain feature extraction approach and exploring the efficiency of feature selection approaches. This shall not only ensure that optimal classifier performance is achieved at lesser computational costs [10], the strenuous parameter tuning stage (usually based on trial and error) can be greatly alleviated.…”

Section: Introductionmentioning

confidence: 99%

“…A variety of feature selection methods have been proposed in the past ranging from filter-based methods to wrapper methods [10], [17], [18]; however, each method has its uniqueness and comes with their respective pitfalls. To mitigate these problems, hybrid feature selection method (a combination of filter and wrapper/embedded methods) yield much better diagnostics results.…”

Section: Introductionmentioning

confidence: 99%

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A Multi-Domain Diagnostics Approach for Solenoid Pumps Based on Discriminative Features

Akpudo

Hur

2020

IEEE Access

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Accurate condition monitoring of industrial cyber-physical systems/components demands the use of reliable fault detection and isolation (FD&I) methodologies. Meta-heuristic algorithms for feature selection have good exploration capability for optimal discriminative feature selection for fault isolation/classification of which the Binary Particle swarm optimization (BPSO) is superior to its counterparts. This study presents a robust approach for vibration-based failure diagnostics of electromagnetic/solenoid pumps which employ a multi-domain feature extraction procedure (statistical time-domain and frequencydomain features, Mel frequency cepstral coefficients, and continuous wavelet coefficients) for capturing linear and nonlinear properties from the signals. Compared with other filter and wrapper methods for supervised feature selection, a hybrid filter-wrapper (Pearson's correlation-BPSO (ρ-BPSO)) feature selection procedure is proposed for global search of optimal discriminative (uncorrelated) features for fault diagnosis with an RBF-kernel support vector machine (SVM*). Subsequently, a practical case study involving five VSC63A5 solenoid pumps at various operating/fault conditions is presented for validating the performance of the proposed approach. Results show the superior performance of the proposed hybrid filterwrapper approach against filter-based and wrapper-based techniques for discriminative feature selection. Also, the proposed ρ-BPSO-SVM* diagnostics model performance was compared with other standard fault isolation/classification methods.

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Section: B Meta-heuristic Methods For Feature Selectionmentioning

confidence: 99%

“…, x a } and x(t) ∈L 2 (R),the wavelet transform W T (a, b) is the convolution of x(t) with a scale and conjugated wavelet ψ(t) [27]. This is shown in (10).…”

Section: ) Continuous Wavelet Transformmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A Multi-Domain Diagnostics Approach for Solenoid Pumps Based on Discriminative Features

Akpudo

Hur

2020

IEEE Access

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“…merupakan teknik pra-pengolahan sangat penting untuk memilih fitur-fitur yang berpengaruh pada sebuah dataset [3]. Pemilihan fitur d igunakan untuk memilih fitur -fitur yang berpengaruh, menghapus fitur tidak relevan pada atribut dataset, waktu ko mputasi menjadi cepat, dan dapat men ingkatkan kinerja dari metode klasifikasi [4], [5]. Teknik pemilihan fitur dibagi menjadi 3 kelo mpok yaitu Filter, Wrapper, dan Embedded [4].…”

Section: Pendahuluanunclassified

Kombinasi Metode Correlated Naive Bayes dan Metode Seleksi Fitur Wrapper untuk Klasifikasi Data Kesehatan

Hairani

Innuddin

2020

Jurnal Teknik Elektro

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Most features of health data that have many irrelevant features can reduce the performance of classification method. One health data that has many attributes is the Pima Indian Diabetes dataset and Thyroid. Diabetes is a deadly disease caused by the increasing of blood sugar because of the body's inability to produce enough insulin and its complications can lead to heart attacks and strokes. The purpose of this research is to do a combination of Correlated Naïve Bayes method and Wrapper-based feature selection to classification of health data. The stages of this research consist of several stages, namely; (1) the collection of Pima Indian Diabetes and Thyroid dataset from UCI Machine Learning Repository, (2) pre-processing data such as transformation, Scaling, and Wrapper-based feature selection, (3) classification using the Correlated Naive Bayes and Naive Bayes methods, and (4) performance test based on its accuracy using the 10-fold cross validation method. Based on the results, the combination of Correlated Naive Bayes method and Wrapper-based feature selection get the best accuracy for both datasets used. For Pima Indian Diabetes dataset, the accuracy is 71,4% and the Thyroid dataset accuracy is 79,38%. Thus, the combination of Correlated Naïve Bayes method and Wrapper-based feature selection result in better accuracy without feature selection with an increase of 4,1% for Pima Indian Diabetes dataset and 0,48% for the Thyroid dataset.

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A Novel Optimization Algorithm: Cascaded Adaptive Neuro-Fuzzy Inference System

Rathnayake

Dang

Hoshino

2021

Int. J. Fuzzy Syst.

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Comparing the Wrapper Feature Selection Evaluators on Twitter Sentiment Classification

Cited by 27 publications

References 15 publications

A Multi-Domain Diagnostics Approach for Solenoid Pumps Based on Discriminative Features

A Multi-Domain Diagnostics Approach for Solenoid Pumps Based on Discriminative Features

Kombinasi Metode Correlated Naive Bayes dan Metode Seleksi Fitur Wrapper untuk Klasifikasi Data Kesehatan

A Novel Optimization Algorithm: Cascaded Adaptive Neuro-Fuzzy Inference System

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