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

Yu, T; Jan, T; Debenham, J; Simoff, S

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

Yu, T Jan, T Debenham, J Simoff, S

Permalink

Publication Type:: Conference Proceeding
Citation:: 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
Issue Date:: 2005-12-01

Open Access

Copyright Clearance Process

Recently Added
In Progress
Open Access

This item is open access.

Adobe PDF

Download full textAdobe PDF (238.55 kB)

View statistics

Full metadata record

Field	Value	Language
dc.contributor.author	Yu, T	en_US
dc.contributor.author	Jan, T	en_US
dc.contributor.author	Debenham, J	en_US
dc.contributor.author	Simoff, S	en_US
dc.date.issued	2005-12-01	en_US
dc.identifier.citation	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	en_US
dc.identifier.isbn	1863657169	en_US
dc.identifier.isbn	9781863657167	en_US
dc.identifier.uri	http://hdl.handle.net/10453/17676
dc.description.abstract	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.	en_US
dc.relation.ispartof	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	en_US
dc.title	Incorporate domain knowledge into support vector machine to classify price impacts of unexpected news	en_US
dc.type	Conference Proceeding
utslib.for	0801 Artificial Intelligence and Image Processing	en_US
dc.location.activity	Sydney, Australia	en_US
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Electrical and Data Engineering
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Software
pubs.organisational-group	/University of Technology Sydney/Strength - AAI - Advanced Analytics Institute Research Centre
utslib.copyright.status	open_access
pubs.publication-status	Published	en_US

Abstract:

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.

Please use this identifier to cite or link to this item:

http://hdl.handle.net/10453/17676