Search Results - (( associative classification _ algorithm ) OR ( java implementation learning algorithm ))
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1
Plagiarism Detection System for Java Programming Assignments by Using Greedy String-Tilling Algorithm
Published 2008“…The prototype system, known as Java Plagiarism Detection System (JPDS) implements the Greedy-String-Tiling algorithm to detect similarities among tokens in a Java source code files. …”
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2
An Educational Tool Aimed at Learning Metaheuristics
Published 2020“…Implemented with Java, this tool provides a friendly GUI for setting the parameters and display the result from where the learner can see how the selected algorithm converges for a particular problem solution. …”
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3
Adoption of machine learning algorithm for analysing supporters and non supporters feedback on political posts / Ogunfolajin Maruff Tunde
Published 2022“…The method was implemented using Java and the results of the simulation were evaluated using five standard performance metrics: accuracy, AUC, precision, recall, and f-Measure. …”
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4
A Feature Ranking Algorithm in Pragmatic Quality Factor Model for Software Quality Assessment
Published 2013“…The methodology used consists of theoretical study, design of formal framework on intelligent software quality, identification of Feature Ranking Technique (FRT), construction and evaluation of FRA algorithm. The assessment of quality attributes has been improved using FRA algorithm enriched with a formula to calculate the priority of attributes and followed by learning adaptation through Java Library for Multi Label Learning (MULAN) application. …”
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5
Breast cancer disease classification using fuzzy-ID3 algorithm based on association function
Published 2022“…The FID3-AF algorithm is a hybridisation of the fuzzy system, the iterative dichotomizer 3 (ID3) algorithm, and the association function. …”
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6
Ant colony algorithm for web page classification
Published 2008“…This paper investigates usage of a swarm intelligence algorithm in the field of the web page classification; Focusing on Persian web pages. …”
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7
Attribute related methods for improvement of ID3 Algorithm in classification of data: A review
Published 2020“…Nevertheless, there are some limitations in ID3 algorithm that can affect the performance in the classification of data. …”
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8
Using fuzzy association rule mining in cancer classification
Published 2011“…A new algorithm has been developed to identify the fuzzy rules and significant genes based on fuzzy association rule mining. …”
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9
Intelligent classification algorithms in enhancing the performance of support vector machine
Published 2019“…The average classification accuracies for the proposed ACOMV–SVM and IACOMV-SVM algorithms are 97.28 and 97.91 respectively. …”
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10
Web-based clustering tool using fuzzy k-mean algorithm / Ahmad Zuladzlan Zulkifly
Published 2019“…This project will use fuzzy k-means clustering algorithm to cluster the data because it is easy to implement and have many advantages. …”
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11
Classification of Diabetes Mellitus (DM) using Machine Learning Algorithms
Published 2021“…The objective of this study is to perform DM classification using various machine learning algorithms using Weka as a tool. …”
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12
Improving Classification of Remotely Sensed Data Using Best Band Selection Index and Cluster Labelling Algorithms
Published 2005“…The advantage of the cluster labelling algorithm compared to co-spectral plot and maximum-likelihood classifier was the algorithm provided a rapid production of high accuracy classification map.…”
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13
An enhanced feature selection technique for classification of group based holy Quran verses
Published 2018“…However, the major setbacks with the existing feature selection techniques are high computational runtime associated with wrapper-based FS techniques and low classification accuracy performance associated with filter-based FS techniques. …”
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14
Selection and optimization of peak features for event-related eeg signals classification / Asrul bin Adam
Published 2017“…At first, a peak classification algorithm is developed based on the general following processes including peak candidate identification, feature extraction, and classification. …”
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15
An extended oversampling method for imbalanced Quranic text classification based on a genetic algorithm
Published 2023“…Overall, this paper highlights the importance of Quranic text classification and the challenges associated with it. …”
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16
A Performance Evaluation of Chi-Square Pruning Techniques in Class Association Rules Optimization
Published 2018“…The noisy data affected support value of an itemset and so it influenced the performance of an associative classification. The performance of associative classification is relied on the classification where the classification is worked based on the class association rules which generated from frequent rule mining process. …”
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17
A Performance Evaluation of Chi-Square Pruning Techniques in Class Association Rules Optimization
Published 2018“…The noisy data affected support value of an itemset and so it influenced the performance of an associative classification. The performance of associative classification is relied on the classification where the classification is worked based on the class association rules which generated from frequent rule mining process. …”
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18
Evaluation and optimization of frequent association rule based classification
Published 2014“…In this paper, a systematic way to evaluate the association rules discovered from frequent itemset mining algorithms, combining common data mining and statistical interestingness measures, and outline an appropriated sequence of usage is presented. …”
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19
The application of neural network data mining algorithm into mixed pixel classification in geographic information system environment
Published 2007“…Region growing segmentation and radial basis function algorithms are considered a powerful tool to minimize the mixed pixel classification error.…”
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20
An enhanced feature selection technique for classification of group-based holy quran verses
Published 2018“…However, the major setbacks with the existing feature selection techniques are high computational runtime associated with wrapper-based FS techniques and low classification accuracy performance associated with filter-based FS techniques. …”
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