TY - GEN
T1 - Adaptive XML tree classification on evolving data streams
AU - Bifet, Albert
AU - Gavaldà, Ricard
PY - 2009/1/1
Y1 - 2009/1/1
N2 - We propose a new method to classify patterns, using closed and maximal frequent patterns as features. Generally, classification requires a previous mapping from the patterns to classify to vectors of features, and frequent patterns have been used as features in the past. Closed patterns maintain the same information as frequent patterns using less space and maximal patterns maintain approximate information. We use them to reduce the number of classification features. We present a new framework for XML tree stream classification. For the first component of our classification framework, we use closed tree mining algorithms for evolving data streams. For the second component, we use state of the art classification methods for data streams. To the best of our knowledge this is the first work on tree classification in streaming data varying with time. We give a first experimental evaluation of the proposed classification method.
AB - We propose a new method to classify patterns, using closed and maximal frequent patterns as features. Generally, classification requires a previous mapping from the patterns to classify to vectors of features, and frequent patterns have been used as features in the past. Closed patterns maintain the same information as frequent patterns using less space and maximal patterns maintain approximate information. We use them to reduce the number of classification features. We present a new framework for XML tree stream classification. For the first component of our classification framework, we use closed tree mining algorithms for evolving data streams. For the second component, we use state of the art classification methods for data streams. To the best of our knowledge this is the first work on tree classification in streaming data varying with time. We give a first experimental evaluation of the proposed classification method.
U2 - 10.1007/978-3-642-04180-8_27
DO - 10.1007/978-3-642-04180-8_27
M3 - Conference contribution
AN - SCOPUS:70350686443
SN - 3642041795
SN - 9783642041792
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 147
EP - 162
BT - Machine Learning and Knowledge Discovery in Databases - European Conference, ECML PKDD 2009, Proceedings
PB - Springer Verlag
T2 - European Conference on Machine Learning and Knowledge Discovery in Databases, ECML PKDD 2009
Y2 - 7 September 2009 through 11 September 2009
ER -