Search Results - (( java application learning algorithm ) OR ( knowledge selection mining algorithm ))
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Combining object-based classification and data mining algorithm to classify urban surface materials from worldview-2 satellite image
Published 2014“…In this study, Data Mining was performed using C4.5 algorithm to select the appropriate attributes for object-based classification. …”
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Combining data mining algorithm and object-based image analysis for detailed urban mapping of hyperspectral images
Published 2014“…The high accuracy of object-based classification can be linked to the knowledge discovery produced by the DM algorithm. This algorithm increased the productivity of OBIA, expedited the process of attribute selection, and resulted in an easy-to-use representation of a knowledge model from a decision tree structure.…”
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First Semester Computer Science Students’ Academic Performances Analysis by Using Data Mining Classification Algorithms
Published 2014“…The research on educational field that involves Data Mining techniques is rapidly increasing. Applying Data Mining techniques in an educational environment are known as Educational Data Mining that aims to discover hidden knowledge and patterns about students’ behaviour. …”
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Multiview Laplacian semisupervised feature selection by leveraging shared knowledge among multiple tasks
Published 2019“…Experiment results on several multimedia applications have shown that the proposed algorithm is competitive compared with the other single-view feature selection algorithms.…”
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Semi-supervised learning for feature selection and classification of data / Ganesh Krishnasamy
Published 2019“…The proposed algorithm is compared with the state-of-the-art feature selection algorithms using three different datasets. …”
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Talent classification using support vector machine technique / Hamidah Jantan, Norazmah Mat Yusof and Mohd Hanapi Abdul Latif
Published 2014“…Database or data warehouse is rich with hidden information that can be used to provide intelligent decision using data mining technique. Data mining is a widely used approach for knowledge discovery in machine learning. …”
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Research Reports -
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Twofold Integer Programming Model for Improving Rough Set Classification Accuracy in Data Mining.
Published 2005“…These tools are known as Data Mining (DM). One aims of DM is to discover decision rules for extracting meaningful knowledge. …”
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ANALYSIS OF CUSTOMER SERVICE BUSINESS PROCESS USING DATA MINING
Published 2020“…Hence, this paper contribute to justify by the basic concepts of data mining, described the selected types and models of algorithms, and the process of data mining by using R Tools.…”
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Final Year Project -
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Identifying significant features and data mining techniques in predicting cardiovascular disease / Mohammad Shafenoor Amin
Published 2018“…This raw data is needed to be processed to make certain decision on various information. Data mining turns a large collection of data into knowledge. …”
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Medical diagnosis using data mining techniques / Shaiful Nizam Zamri
Published 2003“…Secondly, this report will review the literature part which started with basic knowledge of data mining and knowing what the basic information about data mining. …”
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An extended ID3 decision tree algorithm for spatial data
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Data mining techniques for disease risk prediction model: A systematic literature review
Published 2023Conference Paper -
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Comparison Performance of Qualitative Bankruptcy Classification based on Data Mining Algorithms
Published 2018“…Bankruptcy classification and prediction are imperative for informed decision making and problem-solving in actual risk assessment. Knowledge discovery using data mining techniques are commonly applied in bankruptcy classification and prediction. …”
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Towards a better feature subset selection approach
Published 2010“…The selection of the optimal features subset and the classification has become an important issue in the data mining field.We propose a feature selection scheme based on slicing technique which was originally proposed for programming languages.The proposed approach called Case Slicing Technique (CST).Slicing means that we are interested in automatically obtaining that portion 'features' of the case responsible for specific parts of the solution of the case at hand.We show that our goal should be to eliminate the number of features by removing irrelevant once.Choosing a subset of the features may increase accuracy and reduce complexity of the acquired knowledge.Our experimental results indicate that the performance of CST as a method of feature subset selection is better than the performance of the other approaches which are RELIEF with Base Learning Algorithm (C4.5), RELIEF with K-Nearest Neighbour (K-NN), RELIEF with Induction of Decision Tree Algorithm (ID3) and RELIEF with Naïve Bayes (NB), which are mostly used in the feature selection task.…”
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A novel approach to data mining using simplified swarm optimization
Published 2011“…In data mining, data classification and feature selection are considered the two main factors that drive people when making decisions. …”
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Analysis Of Failure In Offline English Alphabet Recognition With Data Mining Approach
Published 2019“…The top three classification algorithms were selected: IBk, LMT and Random Committee for further classification. …”
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Logic Mining Approach: Shoppers’ Purchasing Data Extraction via Evolutionary Algorithm
Published 2023“…The performance of the genetic algorithm with 2-satisfiability-based reverse analysis was measured according to the selected performance evaluation metrics. …”
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Lexicon-based and immune system based learning methods in Twitter sentiment analysis
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Conference or Workshop Item -
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