Search Results - (( process classification problems algorithm ) OR ( _ classification techniques algorithm ))
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1
Enhancing Classification Algorithms with Metaheuristic Technique
Published 2024“…Implementing this process uses classification algorithms such asNaïve Bayes, Support Vector Machine,and Random Forest. …”
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2
Overview of metaheuristic: classification of population and trajectory
Published 2010“…The algorithm techniques can be characterized based on the criteria of the operation of the search process. …”
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3
An enhancement of classification technique based on rough set theory for intrusion detection system application
Published 2019“…In order to improve classification performance problem, feature selection and discretization algorithm are crucial in selecting relevant attributes that could improve classification performance. …”
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4
Classification of breast cancer disease using bagging fuzzy-id3 algorithm based on fuzzydbd
Published 2022“…One of the most powerful machine learning methods to handle classification problems is the decision tree. There are various decision tree algorithms, but the most commonly used are Iterative Dichotomiser 3 (ID3), CART, and C4.5. …”
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5
Hybrid ACO and SVM algorithm for pattern classification
Published 2013“…This is achieved by performing the SVM parameters’ tuning and feature subset selection processes simultaneously. Hybridization algorithms between ACO and SVM techniques were proposed. …”
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6
Talent classification using support vector machine technique / Hamidah Jantan, Norazmah Mat Yusof and Mohd Hanapi Abdul Latif
Published 2014“…Support Vector Machine (SVM) is among the popular learning algorithm for classification in soft computing techniques. …”
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Research Reports -
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Weight Optimization in Recurrent Neural Networks with Hybrid Metaheuristic Cuckoo Search Techniques for Data Classification
Published 2015“…Specifically, some selected benchmark classification problems are used. The simulation results show that the computational efficiency of ERN and BPERN training process is highly enhanced when coupled with the proposed hybrid method.…”
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Incremental continuous ant colony optimization for tuning support vector machine’s parameters
Published 2013“…Ant Colony Optimization originally deals with discrete optimization problems. Hence, in applying Ant Colony Optimization for optimizing Support Vector Machine parameters, which are continuous in nature, the values wil have to be discretized.The discretization process will result in loss of some information and, hence, affects the classification accuracy and seeks time.This paper presents an algorithm to optimize Support Vector Machine parameters using Incremental continuous Ant Colony Optimization without the need to discretize continuous values.Eight datasets from UCI were used to evaluate the performance of the proposed algorithm.The proposed algorithm demonstrates the credibility in terms of classification accuracy when compared to grid search techniques, GA with feature chromosome-SVM, PSO-SVM, and GA-SVM.Experimental results of the proposed algorithm also show promising performance in terms of classification accuracy and size of features subset.…”
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A novel hybrid classification model of genetic algorithms, modified k-Nearest Neighbor and developed backpropagation neural network
Published 2014“…Among numerous artificial intelligence approaches, k-Nearest Neighbor algorithms, genetic algorithms, and artificial neural networks are considered as the most common and effective methods in classification problems in numerous studies. …”
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Intent-IQ: customer’s reviews intent recognition using random forest algorithm
Published 2025“…Two machine learning model is chosen to build the classification models which are Random Forest (RF) algorithm and Multinomial Naïve Bayes (MNB) algorithm. …”
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Modern fuzzy min max neural networks for pattern classification
Published 2019“…To build an efficient classifier model, researchers have introduced hybrid models that combine both fuzzy logic and artificial neural networks. Among these algorithms, Fuzzy Min Max (FMM) neural network algorithm has been proven to be one of the premier neural networks for undertaking the pattern classification problems. …”
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12
Using graph algorithm and classification technique for finding an optimal bus route in time-dependent travel times
Published 2016“…The proposed technique is able to find a least spent travel time path between two nodes in a bus network with time-dependence.Graph algorithm is used to generate all possible paths to reveal the distances.Classification technique is then used to analysis traffic situation in different period of the time.By analysis traffic situation, date, time, week, month, location are used as a main factor for training process in classification technique.From the experimental studies, the proposed technique is able to show the best route from any given node to the final destination depending on the different period of the time.The proposed technique provides significant benefit for traveler to select the best bus route, which is short distance and fast, among generated routes…”
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The classification of hunger behaviour of Lates Calcarifer through the integration of image processing technique and k-Nearest Neighbour learning algorithm
Published 2018“…It was found from the study that the weighted k-NN variation provides the best classification with an accuracy of 86.5%. Therefore, it could be concluded that the proposed integration technique may assist fish farmers in ascertaining fish feeding routine.…”
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Proceeding Paper -
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A comparative study in classification techniques for unsupervised record linkage model
Published 2011“…Problem statement: Record linkage is a technique which is used to detect and match duplicate records which are generated in data integration process. …”
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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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Supervised ANN classification for engineering machined textures based on enhanced features extraction and reduction scheme
Published 2013“…To overcome these problems, an efficient feature extraction and selection technique is required which extracts and reduces the number of selected features and thus improves the classification accuracy. …”
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17
Taylor-Bird Swarm Optimization-Based Deep Belief Network For Medical Data Classification
Published 2022“…However, finding the most appropriate deep learning algorithm for a medical classification problem along with its optimal parameters becomes a difficult task. …”
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18
Text classification using Naive Bayes: An experiment to conference paper
Published 2005“…The basic text classification technique in forum application has been discussed in Sainin (2005a) and Sainin (2005b).The paper explains about the use of the basic naïve Bayes algorithm to classify forum text me ssages into two classes namely clean and bad. …”
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Conference or Workshop Item -
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Integrated ACOR/IACOMV-R-SVM Algorithm
Published 2017“…A direction for ACO is to optimize continuous and mixed (discrete and continuous) variables in solving problems with various types of data. Support Vector Machine (SVM), which originates from the statistical approach, is a present day classification technique. …”
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A hybrid approach for artificial immune recognition system / Mahmoud Reza Saybani
Published 2016“…The components of the AIRS2 algorithm that pose problems will be modified. This thesis proposes three new hybrid algorithms: The FRA-AIRS2 algorithm uses fuzzy logic to improve data reduction capability of AIRS2 and to solve the linearity problem associated with resource allocation of AIRS. …”
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