Enhanced Anytime Algorithm for Induction of Oblivious Decision Trees
نویسندگان
چکیده
Real-time data mining of high-speed and non-stationary data streams has a large potential in such fields as efficient operation of machinery and vehicles, wireless sensor networks, urban traffic control, stock data analysis etc.. These domains are characterized by a great volume of noisy, uncertain data, and restricted amount of resources (mainly computational time). Anytime algorithms offer a tradeoff between solution quality and computation time, which has proved useful in applying artificial intelligence techniques to timecritical problems. In this paper we are presenting a new, enhanced version of an anytime algorithm for constructing a classification model called Information Network (IN). The algorithm improvement is aimed at reducing its computational cost while preserving the same level of model quality. The quality of the induced model is evaluated by its classification accuracy using the standard 10-fold cross validation. The improvement in the algorithm anytime performance is demonstrated on several benchmark data streams.
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