C4.5: programs for machine learning
C4.5: programs for machine learning
The nature of statistical learning theory
The nature of statistical learning theory
Wrappers for performance enhancement and oblivious decision graphs
Wrappers for performance enhancement and oblivious decision graphs
Machine Learning
Text Categorization with Suport Vector Machines: Learning with Many Relevant Features
ECML '98 Proceedings of the 10th European Conference on Machine Learning
Data Mining: Concepts and Techniques
Data Mining: Concepts and Techniques
Data Mining: Practical Machine Learning Tools and Techniques, Second Edition (Morgan Kaufmann Series in Data Management Systems)
A Kernel Level VFS Logger for Building Efficient File System Intrusion Detection System
ICCNT '10 Proceedings of the 2010 Second International Conference on Computer and Network Technology
Local-Learning-Based Feature Selection for High-Dimensional Data Analysis
IEEE Transactions on Pattern Analysis and Machine Intelligence
Modeling Web Logs to Enhance the Analysis of Web Usage Data
DEXA '10 Proceedings of the 2010 Workshops on Database and Expert Systems Applications
Incremental Support Vector Machine Learning: An Angle Approach
CSO '11 Proceedings of the 2011 Fourth International Joint Conference on Computational Sciences and Optimization
A Novel Nonlinear Combination Model Based on Support Vector Machine for Rainfall Prediction
CSO '11 Proceedings of the 2011 Fourth International Joint Conference on Computational Sciences and Optimization
IMIS '11 Proceedings of the 2011 Fifth International Conference on Innovative Mobile and Internet Services in Ubiquitous Computing
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The Web Usage Mining (WUM), a rather recent research field, corresponds to the process of knowledge discovery from databases (KDD) applied to the Web usage data. The quantity of the Web usage data to be analyzed and its poor quality (in particular the abundance of features to be analyzed) are the main problems in WUM. Considering the characteristics of Web log data and functions of every phase included in data preprocessing, this paper establishes a Web log data preprocessing algorithm based on feature selection. The implemented Wrapper Evaluation feature selection method use a Best First Search and a Greedy Stepwise Search and evaluate each of the attribute subsets according to Support Vector Machine learning scheme.