A combination of objective functions and hybrid Krill herd algorithm for text document clustering analysis

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

doi:10.1016/j.engappai.2018.05.003

Cited by 244 publications

(53 citation statements)

References 24 publications

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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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“…Feature selection is an important step in artificial intelligence algorithms and a fundamental unsupervised learning technique [3,5,25]. The quality of the selected features directly affects the final result.…”

Section: Feature Selectionmentioning

confidence: 99%

Hierarchical saliency mapping for weakly supervised object localization based on class activation mapping

Cheng

Zeng

et al. 2020

Multimed Tools Appl

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Weakly supervised object localization is a basic research in the field of computer vision. In this paper, a hierarchical saliency mapping network for object localization is proposed and designed to avoid missing detailed information of potential object. Based on the classical convolution network, we remove the fully connected part and add multiple information extraction branches. The network extracts information from convolution layers of different scales to generate Hierarchical Saliency Map. Hierarchical Saliency Maps that include Hierarchical-Class Activation Map and Hierarchical-Spatial Pyramid Saliency Map fuse deep-level features and low-level features to locate object. The datasets used for testing are Caltech-UCSD Birds 200, Caltech101 and ImageNet. Compared with Class Activation Map and Spatial Pyramid Saliency Map, the localization accuracy has been improved. This method can be used for fine-grained classification, object tracking and other fields.

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“…EQIE-FCM Algorithm (F) Compute the summation of VI DSO (C, U g , m g ) objective function corresponding to all values of C using Equation (14). (G) Compute the normalized value of objective function VI DSO (C, U g , m g ) for all values of C over the range C = 2, 3, ..., c max using Equation (15). for C = 2 : c max do i) Store the fitness of fuzzy partition corresponding to each cluster number C in F g C using Equation (16)…”

Section: B Datasetsmentioning

confidence: 99%

A Generalized Enhanced Quantum Fuzzy Approach for Efficient Data Clustering

et al. 2019

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Data clustering is a challenging task to gain insights into data in various fields. In this paper, an Enhanced Quantum-Inspired Evolutionary Fuzzy C-Means (EQIE-FCM) algorithm is proposed for data clustering. In the EQIE-FCM, quantum computing concept is utilized in combination with the FCM algorithm to improve the clustering process by evolving the clustering parameters. The improvement in the clustering process leads to improvement in the quality of clustering results. To validate the quality of clustering results achieved by the proposed EQIE-FCM approach, its performance is compared with the other quantum-based fuzzy clustering approaches and also with other evolutionary clustering approaches. To evaluate the performance of these approaches, extensive experiments are being carried out on various benchmark datasets and on the protein database that comprises of four superfamilies. The results indicate that the proposed EQIE-FCM approach finds the optimal value of fitness function and the fuzzifier parameter for the reported datasets. In addition to this, the proposed EQIE-FCM approach also finds the optimal number of clusters and more accurate location of initial cluster centers for these benchmark datasets. Thus, it can be regarded as a more efficient approach for data clustering. INDEX TERMS Clustering, quantum computing, evolutionary algorithm, fuzzy set theory, bioinformatics.

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A combination of objective functions and hybrid Krill herd algorithm for text document clustering analysis

Cited by 244 publications

References 24 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

Hierarchical saliency mapping for weakly supervised object localization based on class activation mapping

A Generalized Enhanced Quantum Fuzzy Approach for Efficient Data Clustering

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