Search Results - (( web classification methods algorithm ) OR ( java implication based algorithm ))
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1
Text Extraction Algorithm for Web Text Classification
Published 2010“…This study provides a text extraction algorithm for web text classification. The extraction algorithm consists of three phases namely web page extraction, rule formulation, and algorithm validation. …”
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A new ant based rule extraction algorithm for web classification
Published 2011“…Ant-Miner algorithm is also still lacking in efficiency, accuracy and rule simplicity because of the local minima problem.Therefore, the Ant-Miner algorithm needs to be improved by taking into consideration of the accuracy and rule simplicity criteria so that it could be used to classify Web documents data sets or any large data sets.The best attribute selection method for Web texts categorization is the combination of correlation-based evaluation with random search as the search method.However, this attribute selection method will not give the best performance in attributes reduction. …”
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Adaptive method to improve web recommendation system for anonymous users
Published 2011“…An enhanced classification algorithm is used to assign the current anonymous user to the best web navigation profile. …”
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Comparison on machine learning algorithm to fast detection of malicious web pages
Published 2021“…Selected dataset from well-known resources will be used based on identified features in order to verify the web pages form legitimates ones. Compared to several decision tree method, Random Forest has shown promising and higher sensitivity result towards malicious data which is 98.3% compared to other classification algorithm…”
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Comparison of attribute selection methods for web texts categorization
Published 2010“…This paper presents a study on the performance of attribute selection methods to be used with Ant-Miner algorithm for web text categorization.The new generated data set by each attribute selection method was classified with Ant-Miner to see the performance in terms of predictive accuracy and the number of rules generated.The results of classification were also compared to C4.5 algorithm.…”
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Cyber parental control framework for objectionable web content classification and filtering based on topic modelling using enhanced latent dirichlet allocation / Hamza H. M. Altart...
Published 2023“…Despite substantial advancements in automating web classification that combines web mining and content classification methods, the study identifies a gap in applying advanced machine learning algorithms for superior objectionable web content classification. …”
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An ensemble feature selection method to detect web spam
Published 2018“…Up to now, many methods have been proposed to face with web spam. …”
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Jogging activity recognition using k-NN algorithm
Published 2022“…The k-NN algorithm is a simple and easy-to-implement supervised machine learning algorithm that can be used to solve both classification and regression problems. …”
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Web-based expert system for material selection of natural fiber- reinforced polymer composites
Published 2015“…So, the integration of Artificial Neural Network (ANN) with an Expert System for material classification was explored. The computational tool, Matlab was proposed for classification with Levenberg-Marquardt training algorithm, which provided faster rate of convergence for feed forward network. …”
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10
A Data Mining Approach to Construct Graduates Employability Model in Malaysia
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A new model for iris data set classification based on linear support vector machine parameter's optimization
Published 2020“…One of the outstanding classifications methods in data mining is support vector machine classification (SVM). …”
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Rough set discretization: Equal frequency binning, entropy/MDL and semi naives algorithms of intrusion detection system
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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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K-gen phishguard: an ensemble approach for phishing detection with k-means and genetic algorithm
Published 2025“…This research presents a two-phase phishing detection system by employing unsupervised feature selection and supervised classification. In the first phase, the best set of features is identified by the Genetic algorithm and is utilised by the K-means clustering algorithm to divide the dataset into groups with similar traits. …”
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An enhanced malay named entity recognition using clustering and classification approach for crime textual data analysis
Published 2018“…The methods are Fuzzy C-Means and K-Nearest Neighbors Algorithm. …”
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Intuitive content management system via manipulation and duplication with if-else rules classification
Published 2018“…As a result, ICMS can transform dynamic websites into static websites with faster load speed using manipulation method mixed with data mining classification prediction and Boyer-Moore Horspool algorithm which can be classified, edited, adjustable and searched more precisely. …”
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The Significant Effect of Feature Selection Methods in Spam Risk Assessment Using Dendritic Cell Algorithm
Published 2024Proceedings Paper -
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Phishing image spam classification research trends: Survey and open issues
Published 2020“…This study reviews articles on phishing image spam classification published from 2006 to 2020 based on spam classification application domains, datasets, features sets, spam classification methods, and the measurement metrics adopted in the existing studies. …”
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Named entity recognition using a new fuzzy support vector machine.
Published 2008“…We have improved the precision in NER from text using the new proposed method that calls FSVM for NER. In our method we have employed Support Vector Machine as one of the best machine learning algorithm for classification and we contribute a new fuzzy membership function thus removing the Support Vector Machine’s weakness points in NER precision and multi classification. …”
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