CSDL Home IEEE Transactions on Pattern Analysis & Machine Intelligence 2011 vol.33 Issue No.08 - August

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Issue No.08 - August (2011 vol.33)

pp: 1659-1672

Raúl Fidalgo-Merino , Universidad de Málaga, Málaga

Marlon Núñez , University of Malaga, Malaga

DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/TPAMI.2011.19

ABSTRACT

A new algorithm for incremental construction of binary regression trees is presented. This algorithm, called SAIRT, adapts the induced model when facing data streams involving unknown dynamics, like gradual and abrupt function drift, changes in certain regions of the function, noise, and virtual drift. It also handles both symbolic and numeric attributes. The proposed algorithm can automatically adapt its internal parameters and model structure to obtain new patterns, depending on the current dynamics of the data stream. SAIRT can monitor the usefulness of nodes and can forget examples from selected regions, storing the remaining ones in local windows associated to the leaves of the tree. On these conditions, current regression methods need a careful configuration depending on the dynamics of the problem. Experimentation suggests that the proposed algorithm obtains better results than current algorithms when dealing with data streams that involve changes with different speeds, noise levels, sampling distribution of examples, and partial or complete changes of the underlying function.

INDEX TERMS

Machine learning, mining methods and algorithms, knowledge acquisition, heuristics design.

CITATION

Raúl Fidalgo-Merino, Marlon Núñez, "Self-Adaptive Induction of Regression Trees",

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