Search Results - (( variable optimization svm algorithm ) OR ( automatic classification techniques algorithm ))
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Hybrid ACO and SVM algorithm for pattern classification
Published 2013“…The first two algorithms, ACOR-SVM and IACOR-SVM, tune the SVM parameters while the second two algorithms, ACOMV-R-SVM and IACOMV-R-SVM, tune the SVM parameters and select the feature subset simultaneously. …”
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Formulating new enhanced pattern classification algorithms based on ACO-SVM
Published 2013“…This paper presents two algorithms that integrate new Ant Colony Optimization (ACO) variants which are Incremental Continuous Ant Colony Optimization (IACOR) and Incremental Mixed Variable Ant Colony Optimization (IACOMV) with Support Vector Machine (SVM) to enhance the performance of SVM.The first algorithm aims to solve SVM model selection problem. …”
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Feature selection and model selection algorithm using incremental mixed variable ant colony optimization for support vector machine classifier
Published 2013“…In order to enhance SVM performance, these problems must be solved simultaneously because error produced from the feature subset selection phase will affect the values of the SVM parameters and resulted in low classification accuracy.Most approaches related with solving SVM model selection problem will discretize the continuous value of SVM parameters which will influence its performance.Incremental Mixed Variable Ant Colony Optimization (IACOMV) has the ability to solve SVM model selection problem without discretising the continuous values and simultaneously solve the two problems.This paper presents an algorithm that integrates IACOMV and SVM.Ten datasets from UCI were used to evaluate the performance of the proposed algorithm.Results showed that the proposed algorithm can enhance the classification accuracy with small number of features.…”
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Mixed variable ant colony optimization technique for feature subset selection and model selection
Published 2013“…This paper presents the integration of Mixed Variable Ant Colony Optimization and Support Vector Machine (SVM) to enhance the performance of SVM through simultaneously tuning its parameters and selecting a small number of features.The process of selecting a suitable feature subset and optimizing SVM parameters must occur simultaneously,because these processes affect each ot her which in turn will affect the SVM performance.Thus producing unacceptable classification accuracy.Five datasets from UCI were used to evaluate the proposed algorithm.Results showed that the proposed algorithm can enhance the classification accuracy with the small size of features subset.…”
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Automatic classification of medical x-ray images
Published 2013“…Image representation is one of the major aspects of automatic classification algorithms. In this paper, different feature extraction techniques have been utilized to represent medical X-ray images. …”
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Solving SVM model selection problem using ACOR and IACOR
Published 2013“…In applying ACO for optimizing SVM parameters which are continuous variables, there is a need to discretize the continuously value into discrete values.This discretize process would result in loss of some information and hence affect the classification accuracy.In order to enhance SVM performance and solving the discretization problem, this study proposes two algorithms to optimize SVM parameters using Continuous ACO (ACOR) and Incremental Continuous Ant Colony Optimization (IACOR) without the need to discretize continuous value for SVM parameters.Eight datasets from UCI were used to evaluate the credibility of the proposed integrated algorithm in terms of classification accuracy and size of features subset.Promising results were obtained when compared to grid search technique, GA with feature chromosome-SVM, PSO-SVM, and GA-SVM. …”
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Intelligent classification algorithms in enhancing the performance of support vector machine
Published 2019“…This paper presents two intelligent algorithms that hybridized between ant colony optimization (ACO) and SVM for tuning SVM parameters and selecting feature subset without having to discretize the continuous values. …”
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Integrated ACOR/IACOMV-R-SVM Algorithm
Published 2017“…The first algorithm, ACOR-SVM, will tune SVM parameters, while the second IACOMV-R-SVM algorithm will simultaneously tune SVM parameters and select the feature subset. …”
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Trademark image classification approaches using neural network and rough set theory
Published 2003“…It is essential to incorporate an eficient classification technique to partition the database in order to ensure the performance of an automatic trademark matching system is robust with respect to the increase in the database size. …”
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Automatic topic-based web page classification using deep learning
Published 2023“…The review process looked at the dataset, features, algorithm, pre-processing used in classification of web pages, document representation technique and performance of the web page classification model. …”
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Automatic Topic-Based Web Page Classification Using Deep Learning
Published 2023“…The review process looked at the dataset, features, algorithm, pre-processing used in classification of web pages, document representation technique and performance of the web page classification model. …”
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Breast cancer disease classification using fuzzy-ID3 algorithm with FUZZYDBD method: automatic fuzzy database definition
Published 2021“…FID3 algorithm combined the fuzzy system and decision tree techniques with ID3 algorithm as the decision tree learning. …”
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Dengue classification system using clonal selection algorithm / Karimah Mohd
Published 2012“…This project can be improved by making a comparative study on Artificial Immune System and other techniques or algorithms used to solve dengue classification problems.…”
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Solving Support Vector Machine Model Selection Problem Using Continuous Ant Colony Optimization
Published 2013“…Ant Colony Optimization has been used to solve Support Vector Machine model selection problem.Ant Colony Optimization originally deals with discrete optimization problem.In applying Ant Colony Optimization for optimizing Support Vector Machine parameters which are continuous variables, there is a need to discretize the continuously value into discrete value.This discretize process would result in loss of some information and hence affect the classification accuracy and seeking time.This study proposes an algorithm that can optimize Support Vector Machine parameters using Continuous Ant Colony Optimization without the need to discretize continuous value for Support Vector Machine parameters.Eight datasets from UCI were used to evaluate the credibility of the proposed hybrid algorithm in terms of classification accuracy and size of features subset.Promising results were obtained when compared to grid search technique, GA with feature chromosome-SVM, PSO-SVM, and GA-SVM.…”
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Multichannel optimization with hybrid spectral- entropy markers for gender identification enhancement of emotional-based EEGs
Published 2021“…Finally, the k-nearest neighbors ( kNN) classification technique was used for automatic gender identification of an emotional-based EEG dataset. …”
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Text Extraction Algorithm for Web Text Classification
Published 2010“…Explosive expand of web pages in the World Wide Web makes it difficult for search engine and web directory to give relevant results to the user requirements. Web pages need automatic classification techniques with high classification accuracy. …”
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A study on feature selection and classification techniques for automatic genre classification of traditional Malay music
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Plant leaf recognition algorithm using ant colony-based feature extraction technique
Published 2013“…The aim of this research is to contribute to the measurement of physiological dimensions of plant leaves by the proposed Auto-Measure algorithm to operate in an automatical manner which inherently requires an improvement in automatic feature extraction. …”
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