LOF: identifying density-based local outliers
SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
Efficient algorithms for mining outliers from large data sets
SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
Discovering unexpected information from your competitors' web sites
Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining
Algorithms for Mining Distance-Based Outliers in Large Datasets
VLDB '98 Proceedings of the 24rd International Conference on Very Large Data Bases
LearningPinocchio: adaptive information extraction for real world applications
Natural Language Engineering
Mining web content outliers using structure oriented weighting techniques and N-grams
Proceedings of the 2005 ACM symposium on Applied computing
Resume information extraction with cascaded hybrid model
ACL '05 Proceedings of the 43rd Annual Meeting on Association for Computational Linguistics
Proceedings of the 11th International Conference on Electronic Commerce
International Journal of Computational Science and Engineering
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In the Internet era, the enterprises and companies receive thousands of resumes from the job seekers. Currently available filtering techniques and search services help the recruiters to filter thousands of resumes to few hundred potential ones. Since these filtered resumes are similar to each other, it is difficult to identify the potential resumes by examining each resume. We are investigating the issues related to the development of approaches to improve the performance of resume selection process. We have extended the notion of special features and proposed an approach to identify resumes with special skill information. In the literature, the notion of special features have been applied to improve the process of product selection in E-commerce environment. However, extending the notion of special features for the development of approach to process resumes is a complex task as resumes contain unformatted text or semi-formatted text. In this paper, we have proposed an approach by considering only skills related information of the resumes. The experimental results on the real world data-set of resumes show that the proposed approach has the potential to improve the process of resume selection.