Neural networks and the bias/variance dilemma
Neural Computation
Foundations of statistical natural language processing
Foundations of statistical natural language processing
Data mining: practical machine learning tools and techniques with Java implementations
Data mining: practical machine learning tools and techniques with Java implementations
Machine Learning
Mining the Web for Synonyms: PMI-IR versus LSA on TOEFL
EMCL '01 Proceedings of the 12th European Conference on Machine Learning
A Comparative Study on Feature Selection in Text Categorization
ICML '97 Proceedings of the Fourteenth International Conference on Machine Learning
Data Mining: Concepts and Techniques
Data Mining: Concepts and Techniques
Extracting semantic relations from query logs
Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining
A discretization algorithm based on Class-Attribute Contingency Coefficient
Information Sciences: an International Journal
Introduction to Information Retrieval
Introduction to Information Retrieval
ChiMerge: discretization of numeric attributes
AAAI'92 Proceedings of the tenth national conference on Artificial intelligence
A novel image retrieval model based on the most relevant features
Knowledge-Based Systems
Business Cycle Indication Using Query Logs of Search Engines
3PGCIC '10 Proceedings of the 2010 International Conference on P2P, Parallel, Grid, Cloud and Internet Computing
A rough set approach to feature selection based on power set tree
Knowledge-Based Systems
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Business indices and indicators are used to monitor the regime shifts of business cycles. Generally, the indices and indicators are comprised of various economic variables that are compiled by different government departments. Compiling the variables involves a great deal of data processing, which delays the monitoring of business cycles. In this paper, we propose a novel business cycle surveillance system that utilizes the query logs of search engines for business cycle modeling. The system employs an effective feature selection technique to identify query terms that are representative of business cycles. The selected terms and the frequency count of queries associated with the terms are then integrated to classify the status of business cycles. We use data discretization techniques to reduce the sparseness of query frequencies. Experimental results based on a five-year dataset show that the proposed system can classify the status of business cycles accurately, and the selected query terms reveal interesting human behavior patterns in different business cycles. Unlike economic variables, query logs are readily available through online Web services, so our system can provide business cycle information in a timely manner.