Applying Genetic Algorithms to Information Retrieval Using Vector Space Model

Abualigah, Laith; Hanandeh, Essam Said

doi:10.5121/ijcsea.2015.5102

Cited by 87 publications

(39 citation statements)

References 4 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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“…GA operators are for example genes, chromosomes and populations [25]. In GAs, the population of a candidate solution to an optimization evolves toward better solutions [26]. A genetic algorithm was used in this research to optimize chromosomes containing match (1) and don't care (0) positions.…”

Section: Genetic Algorithmmentioning

confidence: 99%

Optimization of Spaced K-mer Frequency Feature Extraction using Genetic Algorithms for Metagenome Fragment Classification

Pekuwali¹,

Kusuma

Buono

2018

J. ICT Res. Appl.

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K-mer frequencies are commonly used in extracting features from metagenome fragments. In spite of this, researchers have found that their use is still inefficient. In this research, a genetic algorithm was employed to find optimally spaced k-mers. These were obtained by generating the possible combinations of match positions and don't care positions (written as *). This approach was adopted from the concept of spaced seeds in PatternHunter. The use of spaced k-mers could reduce the size of the k-mer frequency feature's dimension. To measure the accuracy of the proposed method we used the naïve Bayesian classifier (NBC). The result showed that the chromosome 111111110001, representing spaced k-mer model [111 1111 10001], was the best chromosome, with a higher fitness (85.42) than that of the k-mer frequency feature. Moreover, the proposed approach also reduced the feature extraction time.

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“…This method does not make any statistical hypothesis on data and, thus, is called unsupervised learning in pattern recognition and data mining. Clustering is mainly used in text clustering [1], search engine optimization [2], landmark selection [3], face recognition [4], and medicine and biology [5].…”

Section: Introductionmentioning

confidence: 99%

Clustering Using an Improved Krill Herd Algorithm

Qin

Liu

2017

Algorithms

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In recent years, metaheuristic algorithms have been widely used in solving clustering problems because of their good performance and application effects. Krill herd algorithm (KHA) is a new effective algorithm to solve optimization problems based on the imitation of krill individual behavior, and it is proven to perform better than other swarm intelligence algorithms. However, there are some weaknesses yet. In this paper, an improved krill herd algorithm (IKHA) is studied. Modified mutation operators and updated mechanisms are applied to improve global optimization, and the proposed IKHA can overcome the weakness of KHA and performs better than KHA in optimization problems. Then, KHA and IKHA are introduced into the clustering problem. In our proposed clustering algorithm, KHA and IKHA are used to find appropriate cluster centers. Experiments were conducted on University of California Irvine (UCI) standard datasets, and the results showed that the IKHA clustering algorithm is the most effective.

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Applying Genetic Algorithms to Information Retrieval Using Vector Space Model

Cited by 87 publications

References 4 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

Optimization of Spaced K-mer Frequency Feature Extraction using Genetic Algorithms for Metagenome Fragment Classification

Clustering Using an Improved Krill Herd Algorithm

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