Zliobaite, I., 2009. Combining Time and Space Similarity for Small Size Learning under Concept Drift. In: the 18th Int. Symposium on Methodologies for Intelligent Systems (ISMIS'09), pp. 412-421.
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Official URL: http://www.springerlink.com/content/t7121070154116...
We present concept drift responsive method for classifier training for sequential data. Relevant instance selection for training is based on similarity to the target observation. Similarity in space and in time is combined. The algorithm determines an optimal training set size. It can be used plugging in different base classifiers. The proposed algorithm shows the best accuracy in the peer group. The algorithm complexity is reasonable for the field applications.
|Item Type:||Conference or Workshop Item (Paper)|
|Subjects:||Generalities > Computer Science and Informatics > Artificial Intelligence|
|Group:||School of Design, Engineering & Computing > Smart Technology Research Centre|
|Deposited By:||Dr Indre Zliobaite LEFT|
|Deposited On:||25 Oct 2011 13:53|
|Last Modified:||07 Mar 2013 15:49|
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