Instance-Based Learning Algorithms
Machine Learning
A practical approach to feature selection
ML92 Proceedings of the ninth international workshop on Machine learning
C4.5: programs for machine learning
C4.5: programs for machine learning
Estimating attributes: analysis and extensions of RELIEF
ECML-94 Proceedings of the European conference on machine learning on Machine Learning
Selection of relevant features and examples in machine learning
Artificial Intelligence - Special issue on relevance
Wrappers for feature subset selection
Artificial Intelligence - Special issue on relevance
Using Rough Sets with Heuristics for Feature Selection
Journal of Intelligent Information Systems
Feature Selection via Discretization
IEEE Transactions on Knowledge and Data Engineering
Distributed Data Mining in Credit Card Fraud Detection
IEEE Intelligent Systems
An adaptation of Relief for attribute estimation in regression
ICML '97 Proceedings of the Fourteenth International Conference on Machine Learning
Generating Accurate Rule Sets Without Global Optimization
ICML '98 Proceedings of the Fifteenth International Conference on Machine Learning
Filters, Wrappers and a Boosting-Based Hybrid for Feature Selection
ICML '01 Proceedings of the Eighteenth International Conference on Machine Learning
Feature Selection for Machine Learning: Comparing a Correlation-Based Filter Approach to the Wrapper
Proceedings of the Twelfth International Florida Artificial Intelligence Research Society Conference
An introduction to variable and feature selection
The Journal of Machine Learning Research
Early detection of insider trading in option markets
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
Margin based feature selection - theory and algorithms
ICML '04 Proceedings of the twenty-first international conference on Machine learning
Efficient Feature Selection via Analysis of Relevance and Redundancy
The Journal of Machine Learning Research
Toward Integrating Feature Selection Algorithms for Classification and Clustering
IEEE Transactions on Knowledge and Data Engineering
IEEE Transactions on Pattern Analysis and Machine Intelligence
An auctioning reputation system based on anomaly
Proceedings of the 12th ACM conference on Computer and communications security
Netprobe: a fast and scalable system for fraud detection in online auction networks
Proceedings of the 16th international conference on World Wide Web
Top 10 algorithms in data mining
Knowledge and Information Systems
A typology of complaints about eBay sellers
Communications of the ACM - The psychology of security: why do good users make bad decisions?
Toward a Comprehensive Model in Internet Auction Fraud Detection
HICSS '08 Proceedings of the Proceedings of the 41st Annual Hawaii International Conference on System Sciences
Reducing internet auction fraud
Communications of the ACM - Web searching in a multilingual world
SNARE: a link analytic system for graph labeling and risk detection
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
A Software Tool for Collecting Data from Online Auctions
ITNG '09 Proceedings of the 2009 Sixth International Conference on Information Technology: New Generations
A proposed data mining approach for internet auction fraud detection
PAISI'07 Proceedings of the 2007 Pacific Asia conference on Intelligence and security informatics
Fraud detection in reputation systems in e-markets using logistic regression
Proceedings of the 2010 ACM Symposium on Applied Computing
A Multiple-Phased Modeling Method to Identify Potential Fraudsters in Online Auctions
ICCRD '10 Proceedings of the 2010 Second International Conference on Computer Research and Development
ACM SIGAPP Applied Computing Review
Catching bad guys with graph mining
XRDS: Crossroads, The ACM Magazine for Students - The Fate of Money
eBay: an E-commerce marketplace as a complex network
Proceedings of the fourth ACM international conference on Web search and data mining
Privacy-enhanced reputation-feedback methods to reduce feedback extortion in online auctions
Proceedings of the first ACM conference on Data and application security and privacy
A novel two-stage phased modeling framework for early fraud detection in online auctions
Expert Systems with Applications: An International Journal
Detecting fraudulent personalities in networks of online auctioneers
PKDD'06 Proceedings of the 10th European conference on Principle and Practice of Knowledge Discovery in Databases
Input feature selection for classification problems
IEEE Transactions on Neural Networks
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While online auctions continue to increase, so does the incidence of online auction fraud. To avoid discovery, fraudsters often disguise themselves as honest members by imitating normal trading behaviors. Therefore, maintaining vigilance is not sufficient to prevent fraud. Participants in online auctions need a more proactive approach to protect their profits, such as an early fraud detection system. In practice, both accuracy and timeliness are equally important when designing an effective detection system. An instant but incorrect message to the users is not acceptable. However, a lengthy detection procedure is also unsatisfactory in assisting traders to place timely bids. The detection result would be more helpful if it can report potential fraudsters as early as possible. This study proposes a new early fraud detection method that considers accuracy and timeliness simultaneously. To determine the most appropriate attributes that distinguish between normal traders and fraudsters, a modified wrapper procedure is developed to select a subset of attributes from a large candidate attribute pool. Using these attributes, a complement phased modeling procedure is then proposed to extract the features of the latest part of traders' transaction histories, reducing the time and resources needed for modeling and data collection. An early fraud detection model can be obtained by constructing decision trees or by instance-based learning. Our experimental results show that the performance of the selected attributes is superior to other attribute sets, while the hybrid complement phased models markedly improve the accuracy of fraud detection.