Search Results - (( java application based algorithm ) OR ( rule generation learning algorithm ))
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1
New Learning Models for Generating Classification Rules Based on Rough Set Approach
Published 2000“…Also, the proposed models for learning in data sets generated the classification rules faster than other methods. …”
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2
A Hybrid of Ant Colony Optimization Algorithm and Simulated Annealing for Classification Rules
Published 2013“…Previous studies have shown that ACO is a promising machine learning technique to generate classification rules. …”
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3
Frequent Lexicographic Algorithm for Mining Association Rules
Published 2005“…The primary concept of association rule algorithms consist of two phase procedure. In the first phase, all frequent patterns are found and the second phase uses these frequent patterns in order to generate all strong rules. …”
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4
Twofold Integer Programming Model for Improving Rough Set Classification Accuracy in Data Mining.
Published 2005“…Total rules number, rules length and rules accuracy for the generation rules are recorded. …”
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5
Forecasting hydrological parameters for reservoir system utilizing artificial intelligent models and exploring their influence on operation performance
Published 2019“…Several research efforts have been developed to generate optimal operation rules for dam and reservoir systems utilizing different optimization algorithms. …”
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6
Self learning neuro-fuzzy modeling using hybrid genetic probabilistic approach for engine air/fuel ratio prediction
Published 2017“…The model was compared to other learning algorithms for NFS such as Fuzzy c-means (FCM) and grid partition algorithm. …”
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7
An Apriori-based Data Analysis on Suspicious Network Event Recognition
Published 2019“…Furthermore, two algorithms preserve the logical property 'completeness,' so they generate rules without excess and deficiency. …”
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8
Enhancing predictive crime mapping model using association rule mining for geographical and demographic structure
Published 2014“…The other 40% of the dataset is used to test generated rules. A simple program of C++ is implemented using Microsoft Visual Studio to test generated rules until accuracy of performance is obtained. …”
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9
An ELM-based single input rule module and its application in power generation
Published 2023“…Extreme Learning Machine (ELM) is widely known as an effective learning algorithm than the conventional learning methods from the point of learning speed as well as generalization. …”
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Fuzzy rules reduction using rough set approach
Published 2003“…This paper presents the use of Rough Set approach to compute reducts and generate concise fuzzy rules from a fuzzy rule base system of a student model. …”
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11
RSA Encryption & Decryption using JAVA
Published 2006“…The implementation of this project will be based on Rapid Application Design Methodology (RAD) and will be more focusing on research and finding, ideas and the implementation of the algorithm, and finally running and testing the algorithm. …”
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12
Hybrid ant colony optimization and genetic algorithm for rule induction
Published 2020“…In our proposed hybrid ACO/GA algorithm, the ACO is responsible for generating classification rules and the GA improves the classification rules iteratively using the principles of multi-neighborhood structure (i.e., mutation and crossover) procedures to overcome the local optima problem. …”
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13
Data mining reduction methods and performances of rules
Published 2009“…In data mining the accuracy of models are associated with the strength of the rules.However, most machine learning techniques produce a large number of rules.The consequence is with large number of rules generated,processing time is much longer. …”
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14
Operating a reservoir system based on the shark machine learning algorithm
Published 2018“…In the current study, the shark machine learning algorithm (SMLA) is proposed to develop an optimal rule for operating the reservoir. …”
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Penggunaan penggugusan subtraktif bagi menjana peraturan kabur
Published 2005“…The basic idea in this paper work is how to learn and generate the optimum rules that required controlling input without decreasing the control quality. …”
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17
Comparing the knowledge quality in rough classifier and decision tree classifier
Published 2008“…Theoretically, different classifiers will generate different sets of rules via knowledge even though they are implemented to the same classification problem.Hence, the aim of this paper is to investigate the quality of knowledge produced by Rc and DTc when similar problems are presented to them.In this case, four important performance metrics are used as comparison, the accuracy of classification, rules quantity, rules length and rules coverage.Five dataset from UCI Machine Learning are chosen and then mined using Rc toolkit namely ROSETTA while C4.5 algorithm in WEKA application is chosen as DTc rule generator. …”
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18
A Hybrid Rough Sets K-Means Vector Quantization Model For Neural Networks Based Arabic Speech Recognition
Published 2002“…A vector quantization model that incorporate rough sets attribute reduction and rules generation with a modified version of the K-means clustering algorithm was developed, implemented and tested as a part of a speech recognition framework, in which the Learning Vector Quantization (LVQ) neural network model was used in the pattern matching stage. …”
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19
An initial state of design and development of intelligent knowledge discovery system for stock exchange database
Published 2004“…Generally our clustering algorithm consists of two steps including training and running steps.The training step is conducted for generating the neural network knowledge based on clustering.In running step, neural network knowledge based is used for supporting the Module in order to generate learned complete data, transformed data and interesting clusters that will help to generate interesting rules.…”
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20
Breast cancer disease classification using fuzzy-ID3 algorithm with FUZZYDBD method: automatic fuzzy database definition
Published 2021“…The inference system of FID3 algorithm is simple with direct extraction of rules from generated tree to determine the classes for the new input instances. …”
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