Incorporate domain knowledge into support vector machine to classify price impacts of unexpected news

Publication Type:
Conference Proceeding
AusDM 2005 Proc. - 4th Australasian Data Mining Conf. - Collocated with the 18th Australian Joint Conf. on Artificial Intelligence, AI 2005 and the 2nd Australian Conf. on Artifical Life, ACAL 2005, 2005, pp. 1 - 11
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We present a novel approach for providing approximate answers to classifying news events into simple three categories. The approach is based on the authors' previous research: incorporating domain knowledge into machine learning [1], and initially explore the results of its implementation for this particular field. In this paper, the process of constructing training datasets is emphasized, and domain knowledge is utilized to pre-process the dataset. The piecewise linear fitting etc. is used to label the outputs of the training datasets, which is fed into a classifier built by support vector machine, in order to learn the interrelationship between news events and volatility of the given stock price. © 2013.
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