Search Results - (( java implementation cell algorithm ) OR ( using rough learning algorithm ))
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New Learning Models for Generating Classification Rules Based on Rough Set Approach
Published 2000“…Recently, different models were used to generate knowledge from vague and uncertain data sets such as induction decision tree, neural network, fuzzy logic, genetic algorithm, rough set theory, and others. …”
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A Hybrid Rough Sets K-Means Vector Quantization Model For Neural Networks Based Arabic Speech Recognition
Published 2002“…Classification rules were generated from training feature vectors set, and a modified form of the standard voter classification algorithm, that use the rough sets generated rules, was applied. …”
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Fuzzy rules reduction using rough set approach
Published 2003“…Johnson reducer and Genetic Algorithm reducer are the methods used for computing reducts. …”
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Discovering decision algorithm of distance protective relay based on rough set theory and rule quality measure
Published 2011“…The discovered decision algorithm and association rule from the Rough-Set based data mining had been compared with and successfully validated by those discovered using the benchmarking Decision-Tree based data mining strategy. …”
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Hybrid Classification Algorithm For Denial Of Service Attack Detection Using Rough Set Theory And Artificial Immune
Published 2024thesis::master thesis -
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Rough-Set-and-Genetic-Algorithm based data mining and Rule Quality Measure to hypothesize distance protective relay operation characteristics from relay event report
Published 2011“…Firstly, the data mining approach of the integrated-Rough-Set-and-Genetic-Algorithm is used to discover the relay CD-decision algorithm. …”
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Rough Neural Networks Architecture For Improving Generalization In Pattern Recognition
Published 2004“…The neural network learns the rough set’s upper and lower approximations as feature extractors simultaneously with classification. …”
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Case Slicing Technique for Feature Selection
Published 2004“…CST was compared to other selected classification methods based on feature subset selection such as Induction of Decision Tree Algorithm (ID3), Base Learning Algorithm K-Nearest Nighbour Algorithm (k-NN) and NaYve Bay~sA lgorithm (NB). …”
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An Improvement on Extended Kalman Filter for Neural Network Training
Published 2005“…This study explored the training of a neural network inference system using the extended Kalman filter (EKF) learning algorithm. …”
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Twofold Integer Programming Model for Improving Rough Set Classification Accuracy in Data Mining.
Published 2005“…The accuracy for rules and classification resulted from the TIP method are compared with other methods such as Standard Integer Programming (SIP) and Decision Related Integer Programming (DRIP) from Rough Set, Genetic Algorithm (GA), Johnson reducer, HoltelR method, Multiple Regression (MR), Neural Network (NN), Induction of Decision Tree Algorithm (ID3) and Base Learning Algorithm (C4.5); all other classifiers that are mostly used in the classification tasks. …”
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Image classification of Aedes mosquitoes using transfer learning / Zetty Ilham Abdullah
Published 2021“…The advancement and rapid growth of machine learning field should not overlook this issue. Transfer learning concept in machine learning has been shown to improve learning of the targeted task by extending the original algorithm with knowledge gathered from the initial training to improve the performance of new model. …”
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Knowledge discovery in distance relay event report: a comparative data-mining strategy of rough set theory with decision tree
Published 2010“…This paper addresses these issues by intelligently divulging the knowledge hidden in the relay recorded event report using a data-mining strategy based on rough set theory and a rule-quality measure under supervised learning to discover the relay decision algorithm and association rule. …”
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Development of lung cancer prediction system using meta-heuristic optimized deep learning model
Published 2023“…Finally, the classification is implemented using an ensemble classifier, deep learning instantaneously trained a neural network and an Autoencoder-based Recurrent Neural Network (ARNN) classification algorithm. …”
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EEG-Based Person Authentication Modelling Using Incremental Fuzzy-Rough Nearest Neighbour Technique
Published 2016“…The IncFRNN algorithm is able to control the size of training pool using predefined window size threshold. …”
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An artificial immune system model as talent performance predictor / Siti ‘Aisyah Sa’dan, Hamidah Jantan and Mohd Hanapi Abdul Latif
Published 2016“…Soft computing techniques, such as bio-inspired algorithms, can be used for information processing by employing methods which are capable to deal with imprecision and uncertainty. …”
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Cleansing of inconsistent sample in linear regression model based on rough sets theory
Published 2023“…The linear regression model is one of the most common and easiest algorithms used in machine learning for predictive analysis purposes. …”
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Cleansing of inconsistent sample in linear regression model based on rough sets theory
Published 2023“…The linear regression model is one of the most common and easiest algorithms used in machine learning for predictive analysis purposes. …”
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