A new feature selection method to improve the document clustering using particle swarm optimization algorithm

Abualigah, Laith; Khader, Ahamad Tajudin; Hanandeh, Essam Said

doi:10.1016/j.jocs.2017.07.018

Cited by 412 publications

(103 citation statements)

References 20 publications

Supporting

Mentioning

101

Contrasting

Unclassified

Order By: Relevance

“…Posteriormente se evaluará el contenido de los mismos, es decir como agrupó las observaciones normales y las defectuosas ya que de antemano se conoce cuáles de estas observaciones pertenecen a una u otra clase. Para evaluar este algoritmo se usaron diversas métricas como la exactitud (Ac), la precisión (Pr), exhaustividad (Re) y la conocida como F-score [2], las cuales se calculan de la siguiente manera. Como se observa, los valores de las métricas alcanzados por k-medias son bajos para implementar dicho algoritmo para la detección de fallas.…”

Section: Resultados Experimentalesunclassified

Selección óptima de variables mediante metaheurísticas binarias para la detección de fallas en un proceso industrial multivariado usando aprendizaje de máquina

Acosta¹,

Reséndiz‐Flores²,

García-Calvillo³

2019

RCS

View full text Add to dashboard Cite

Resumen. Este trabajo presenta la comparación entre la optimización por enjambre de partículas y el algoritmo de búsqueda gravitacional en sus versiones binarias en combinación con la distancia Mahalanobis para la selecciónóptima de variables en un proceso de soldadura de discos de soporte para el mecanismo de los asientos en el automóvil. Los resultados muestran que la metodología implementada es capaz de seleccionar un subconjunto de variables capaz de mantener e incluso aumentar la exactitud en la detección de fallas en el proceso, así como brindar a los encargados del proceso las variables que intervienen en la variabilidad del mismo. Lo anterior se valida mediante la implementación de 16 algoritmos y enfoques de aprendizaje automático supervisado y otro no supervisado ampliamente utilizados para tareas de reconocimiento de patrones y detección de fallas.Palabras clave: metaheurísticas binarias, distancia Mahalanobis, aprendizaje automático.Abstract. This work presents the comparison between binary particle swarm optimization and binary gravitational search algorithm in combination with the Mahalanobis distance for the optimal feature selection in a welding process of support discs for the seat mechanism in the car. The results show that the implemented methodology is able to select a subset of variables capable of maintaining and even increasing the accuracy for fault detection in the process. This is validated through 37

show abstract

Section: Resultados Experimentalesunclassified

Selección óptima de variables mediante metaheurísticas binarias para la detección de fallas en un proceso industrial multivariado usando aprendizaje de máquina

Acosta¹,

Reséndiz‐Flores²,

García-Calvillo³

2019

RCS

View full text Add to dashboard Cite

show abstract

“…This opens up a whole new field of research where optimization of the learning process is required to enable a comprehensive capturing of the extracted features' embedded knowledge. One competent way to tackle such problem is to use meta-heuristic FS method [23][24][25][26][27][28][29][30][31][32][33] which intelligently selects only the relevant features without loss of any valuable information. This method assumes that this reduced set of features carries significant information about the audio signal and is enough for the model to identify the different spoken languages while maintaining a high accuracy level.…”

Section: Motivation and Contributionsmentioning

confidence: 99%

“…To establish the superiority of the proposed FS method, this algorithm is compared with 15 state-of-the-art metaheuristic FS algorithms, namely: 1) Binary Genetic Algorithm (BGA) [53] 2) Binary Particle Swarm Optimization (BPSO) [54][55] 3) Binary Gravitational Search Algorithm (BGSA) [ [69] This comparison is done for all the databases namely CSS10, VoxForge and Indic TTS. For all of the above mentioned algorithms, the five classifiers have been again used namely SVM, k-NN, MLP, NB and MLP.…”

Section: Comparative Studymentioning

confidence: 99%

Hybrid Feature Selection Method Based on Harmony Search and Naked Mole-Rat Algorithms for Spoken Language Identification From Audio Signals

et al. 2020

View full text Add to dashboard Cite

This era is dominated by artificial intelligence and its various applications-one of which is Spoken Language Identification (S-LID) which has always been a challenging issue and an important research area in the domain of speech signal processing. This paper deals with SLID to be used for Human-Computer Interaction (HCI) based applications by attempting to classify various languages from three multilingual databases namely CSS10: A Collection of Single Speaker Speech Datasets for 10 Languages, VoxForge and Indian Institute of Technology, Madras (IIT-Madras) speech corpus database by extracting their Mel-Spectrogram features and Relative Spectral Transform-Perceptual Linear Prediction (RASTA-PLP) features. A new hybrid Feature Selection (FS) algorithm have been developed using the versatile Harmony Search (HS) algorithm and a new nature-inspired algorithm called Naked Mole-Rat (NMR) algorithm to select the best subset of features and reduce the model complexity to help it train faster. This selected feature set is fed to five classifiers namely Support Vector Machine (SVM), k-Nearest Neighbor (k-NN), Multi-layer Perceptron (MLP), Naïve Bayes (NB) and Random Forest (RF). The evaluation measures used in this paper are precision, recall, f1-score, classification accuracy and number of selected features. An accuracy of 99.89% on CSS10, 98.22% on VoxForge and 99.75% on IIT-Madras speech corpus databases is achieved using RF. Furthermore, the proposed algorithm is found to outperform 15 standard meta-heuristic FS algorithms.

show abstract

“…Table I demonstrates the WCAG 2.0 conformance levels. Other optimization techniques can be used [7][8][9][10][11][12][13]. All SCs of level A are satisfied.…”

Section: Wcag 20mentioning

confidence: 99%

The Accessibility Diagnosis on Jordan E-Government Website

Abuaddous¹

2019

IJSAIT

View full text Add to dashboard Cite

The aim of this paper is to evaluate Jordan E-Government website from the accessibility perspective. An evaluation was made to diagnose the website accessibility using online-automated tool named TAW to provide in-depth technical direction on HTML issues relating to accessibility. The results showed significant issues and the website lacks accessibility. Therefore, the website needs further improvements to improve its quality in order to make this website accessible to everyone including people with certain disabilities.

show abstract

A new feature selection method to improve the document clustering using particle swarm optimization algorithm

Cited by 412 publications

References 20 publications

Selección óptima de variables mediante metaheurísticas binarias para la detección de fallas en un proceso industrial multivariado usando aprendizaje de máquina

Selección óptima de variables mediante metaheurísticas binarias para la detección de fallas en un proceso industrial multivariado usando aprendizaje de máquina

Hybrid Feature Selection Method Based on Harmony Search and Naked Mole-Rat Algorithms for Spoken Language Identification From Audio Signals

The Accessibility Diagnosis on Jordan E-Government Website

Contact Info

Product

Resources

About