Mining the Web: Discovering Knowledge from HyperText Data
Mining the Web: Discovering Knowledge from HyperText Data
Tackling concept drift by temporal inductive transfer
SIGIR '06 Proceedings of the 29th annual international ACM SIGIR conference on Research and development in information retrieval
Training linear SVMs in linear time
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
Utility analysis for topically biased PageRank
Proceedings of the 16th international conference on World Wide Web
A Novel Web Page Filtering System by Combining Texts and Images
WI '06 Proceedings of the 2006 IEEE/WIC/ACM International Conference on Web Intelligence
Web page classification: Features and algorithms
ACM Computing Surveys (CSUR)
SED: supervised experimental design and its application to text classification
Proceedings of the 33rd international ACM SIGIR conference on Research and development in information retrieval
Temporally-aware algorithms for document classification
Proceedings of the 33rd international ACM SIGIR conference on Research and development in information retrieval
A survey of hierarchical classification across different application domains
Data Mining and Knowledge Discovery
Genetic algorithm based solution to dead-end problems in robot navigation
International Journal of Computer Applications in Technology
Inspection of surface defects in copper strip using multivariate statistical approach and SVM
International Journal of Computer Applications in Technology
Hybrid dynamic k-nearest-neighbour and distance and attribute weighted method for classification
International Journal of Computer Applications in Technology
Combining lexical and structural information for static bug localisation
International Journal of Computer Applications in Technology
Short text classification using very few words
SIGIR '12 Proceedings of the 35th international ACM SIGIR conference on Research and development in information retrieval
The Mining of Term Semantic Relationships and its Application in Text Classification
ICICTA '12 Proceedings of the 2012 Fifth International Conference on Intelligent Computation Technology and Automation
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Classification is essential to many web applications such as focused crawling, search engine, recommendation, content filter, knowledge discovery, etc. Although traditional classification algorithms have many virtues, the unstructured and big web data application presents challenges to these traditional algorithms. Moreover, the traditional segmentation processing usually needs more time and a complete lexicon. This work focuses on an interesting and promising approach that may enhance the classification performance of web and big data applications. Based on the non-segment and the classified-centre-vector, the proposed algorithm can meet the request on big data classifications. By using positive and negative modification, it can revise the potential data bias. The experimental results and the analysis show the feasibility of this approach.