TY - GEN
T1 - Stream classification with recurring and novel class detection using class-based ensemble
AU - Al-Khateeb, Tahseen
AU - Masud, Mohammad M.
AU - Khan, Latifur
AU - Aggarwal, Charu
AU - Han, Jiawei
AU - Thuraisingham, Bhavani
PY - 2012
Y1 - 2012
N2 - Concept-evolution has recently received a lot of attention in the context of mining data streams. Concept-evolution occurs when a new class evolves in the stream. Although many recent studies address this issue, most of them do not consider the scenario of recurring classes in the stream. A class is called recurring if it appears in the stream, disappears for a while, and then reappears again. Existing data stream classification techniques either misclassify the recurring class instances as another class, or falsely identify the recurring classes as novel. This increases the prediction error of the classifiers, and in some cases causes unnecessary waste in memory and computational resources. In this paper we address the recurring class issue by proposing a novel "class-based" ensemble technique, which substitutes the traditional "chunkbased" ensemble approaches and correctly distinguishes between a recurring class and a novel one. We analytically and experimentally confirm the superiority of our method over state-of-the-art techniques.
AB - Concept-evolution has recently received a lot of attention in the context of mining data streams. Concept-evolution occurs when a new class evolves in the stream. Although many recent studies address this issue, most of them do not consider the scenario of recurring classes in the stream. A class is called recurring if it appears in the stream, disappears for a while, and then reappears again. Existing data stream classification techniques either misclassify the recurring class instances as another class, or falsely identify the recurring classes as novel. This increases the prediction error of the classifiers, and in some cases causes unnecessary waste in memory and computational resources. In this paper we address the recurring class issue by proposing a novel "class-based" ensemble technique, which substitutes the traditional "chunkbased" ensemble approaches and correctly distinguishes between a recurring class and a novel one. We analytically and experimentally confirm the superiority of our method over state-of-the-art techniques.
KW - Novel class
KW - Recurring class
KW - Stream classification
UR - http://www.scopus.com/inward/record.url?scp=84874099642&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84874099642&partnerID=8YFLogxK
U2 - 10.1109/ICDM.2012.125
DO - 10.1109/ICDM.2012.125
M3 - Conference contribution
AN - SCOPUS:84874099642
SN - 9780769549057
T3 - Proceedings - IEEE International Conference on Data Mining, ICDM
SP - 31
EP - 40
BT - Proceedings - 12th IEEE International Conference on Data Mining, ICDM 2012
T2 - 12th IEEE International Conference on Data Mining, ICDM 2012
Y2 - 10 December 2012 through 13 December 2012
ER -