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Incremental continuous ant colony optimization for tuning support vector machine’s parameters
Published 2013“…Support Vector Machines are considered to be excellent patterns classification techniques. The process of classifying a pattern with high classification accuracy counts mainly on tuning Support Vector Machine parameters which are the generalization error parameter and the kernel function parameter.Tuning these parameters is a complex process and Ant Colony Optimization can be used to overcome the difficulty. …”
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A numerical method for frequent pattern mining
Published 2009“…The PC_Miner algorithm traverses the PC_Tree by using an efficient pruning technique. …”
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Optimizing support vector machine parameters using continuous ant colony optimization
Published 2012“…Hence, in applying Ant Colony Optimization for optimizing Support Vector Machine parameters, which are continuous parameters, there is a need to discretize the continuous value into a discrete value.This discretization process results in loss of some information and, hence, affects the classification accuracy and seek time.This study proposes an algorithm to optimize Support Vector Machine parameters using continuous Ant Colony Optimization without the need to discretize continuous values for Support Vector Machine parameters.Seven datasets from UCI were used to evaluate the performance of the proposed hybrid algorithm.The proposed algorithm demonstrates the credibility in terms of classification accuracy when compared to grid search techniques.Experimental results of the proposed algorithm also show promising performance in terms of computational speed.…”
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Using unique-prime-factorization theorem to mine frequent patterns without generating tree
Published 2011“…In this study we introduce an algorithm to generate frequent patterns without generating a tree and therefore improve the time complexity and memory complexity as well. …”
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An Efficient Data Structure for General Tree-Like Framework in Mining Sequential Patterns Using MEMISP
Published 2007“…In this paper, we introduce a general tree-like data structure framework for mining sequential patterns. …”
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Separator Database and SPM Tree Framework for Mining Sequential Patterns Using Prefixspan with Pseudoprojection
Published 2008“…A comprehensive performance study has been reported that PrefixSpan, one of the sequential pattern mining algorithms, outperforms GSP, SPADE, as well as FreeSpan in most cases, and PrefixSpan integrated with pseudoprojection technique is the fastest among those tested algorithms. …”
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Prime-based method for interactive mining of frequent patterns
Published 2010“…Since rerunning the mining algorithms from scratch can be very time consuming, researchers have introduced interactive mining to find proper patterns by using the current mining model with various minsup. …”
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Big data clustering using grid computing and ant-based algorithm
Published 2013“…This paper presents a framework for big data clustering which utilizes grid technology and ant-based algorithm.…”
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Separator Database and SPM Tree Framework for Mining Sequential Patterns Using Prefixspan with Pseudoprojection
Published 2008“…A comprehensive performance study has been reported that PrefixSpan, one of the sequential pattern mining algorithms, outperforms GSP, SPADE, as well as FreeSpan in most cases, and PrefixSpan integrated with pseudoprojection technique is the fastest among those tested algorithms. …”
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Ant system-based feature set partitioning algorithm for classifier ensemble construction
Published 2016“…All of these approaches attempt to generate diversity in the ensemble.However, classifier ensemble construction still remains a problem because there is no standard guideline in constructing a set of accurate and diverse classifiers. In this study, Ant system-based feature set partitioning algorithm for classifier ensemble construction is proposed.The Ant System Algorithm is used to form an optimal feature set partition of the original training set which represents the number of classifiers.Experiments were carried out to construct several homogeneous classifier ensembles using nearest mean classifier, naive Bayes classifier, k-nearest neighbor and linear discriminant analysis as base classifier and majority voting technique as combiner. …”
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Ant colony algorithm for text classification in multicore-multithread environment / Ahmad Nazmi Fadzal
Published 2017“…Pheromone concept is the main criterion that distinguish ACO to other algorithms. Based on the concept, pheromone saturation is used to combine stackable solution pattern that is discovered while straying to different term node to build a path. …”
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Hybrid ACO and SVM algorithm for pattern classification
Published 2013“…Ant Colony Optimization (ACO) is a metaheuristic algorithm that can be used to solve a variety of combinatorial optimization problems. …”
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Fast discovery of long patterns for association rules
Published 2003“…An algorithm called Flex for identifying frequent patterns especially efficient when the patterns are long is proposed by successive construction of the nodes lexicographic tree. …”
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Frequent Lexicographic Algorithm for Mining Association Rules
Published 2005“…The algorithm involved the construction of the nodes of a lexicographic tree that represent frequent patterns. …”
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Traditional marble game using ant colony optimization / Muhammad Izzat Imran Che Isa
Published 2017“…The study of traditional marble game will be implemented in a game prototype using Ant Colony Optimization (ACO). ACO technique is used for searching method in order to find the nearest marble that can be selected to be shot. …”
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Pattern Recognition for Human Diseases Classification in Spectral Analysis
Published 2022“…To conclude, many pattern recognition algorithms have the potential to overcome each of their distinct limits, and there is also the option of combining all of these algorithms to create an ensemble of methods.…”
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Determination of tree height based on tree crown using algorithm derived from UAV imagery / Suzanah Abdullah ... [et al.]
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Hybrid ant colony system algorithm for static and dynamic job scheduling in grid computing
Published 2015“…The proposed algorithms were evaluated against twelve metaheuristic algorithms in static (expected time to compute model) and dynamic (distribution pattern) grid computing environments. …”
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