Feature Selection and Enhanced Krill Herd Algorithm for Text Document Clustering

Abualigah, Laith

doi:10.1007/978-3-030-10674-4

Cited by 387 publications

(90 citation statements)

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“…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%

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

et al. 2020

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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.

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Section: Motivation and Contributionsmentioning

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

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“…DLN is a way of penalizing the term weights for a document in accordance with its length. DLN has been one of the central topics of interest in IR and document clustering theory and applications for many years [2,22,23]. These include cosine normalization, relative frequency, maximum term frequency, mean term frequency, probability normalization, byte length normalization, and likelihood of relevance.…”

Section: Literature Reviewmentioning

confidence: 99%

Document Length Variation in the Vector Space Clustering of News in Arabic: A Comparison of Methods

Omar¹,

Hamouda²

2020

IJACSA

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This article is concerned with addressing the effect of document length variation on measuring the semantic similarity in the text clustering of news in Arabic. Despite the development of different approaches for addressing the issue, there is no one strong conclusion recommending one approach. Furthermore, many of these have not been tested for the clustering of news in Arabic. The problem is that different length normalization methods can yield different analyses of the same data set, and that there is no obvious way of selecting the best one. The choice of an inappropriate method, however, has negative impacts on the accuracy and thus the reliability of clustering performance. Given the lack of agreement and disparity of opinions, we set out to comprehensively evaluate the existing normalization techniques to prove empirically which one is the best for the normalization of text length to improve the text clustering performance of news in Arabic. For this purpose, a corpus of 693 stories representing different categories and of different lengths is designed. Data is analyzed using different document length normalization methods along with vector space clustering (VSC), and then the analysis on which the clustering structure agrees most closely with the bibliographic information of the news stories is selected. The analysis of the data indicates that the clustering structure based on the byte length normalization method is the most accurate one. One main problem, however, with this method is that the lexical variables within the data set are not ranked which makes it difficult for retaining only the most distinctive lexical features for generating clustering structures based on semantic similarity. As thus, the study proposes the integration of TF-IDF for ranking the words within all the documents so that only those with the highest TF-IDF values are retained. It can be finally concluded that the proposed model proved effective in improving the function of the byte normalization method and thus on the performance and reliability of news clustering in Arabic. The findings of the study can also be extended to IR applications in Arabic. The proposed model can be usefully used in supporting the performance of the retrieval systems of Arabic in finding the most relevant documents for a given query based on semantic similarity, not document length.

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“…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

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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.

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Feature Selection and Enhanced Krill Herd Algorithm for Text Document Clustering

Cited by 387 publications

References 0 publications

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

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

Document Length Variation in the Vector Space Clustering of News in Arabic: A Comparison of Methods

The Accessibility Diagnosis on Jordan E-Government Website

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