Title :
A new classification approach for handling new outcomes
Author :
Li, Yingrong ; Kolesnikova, Anastasiya ; Don Lee, Won
Author_Institution :
Dept. of Comput. Sci. & Eng., Chungnam Nat. Univ., Daejeon
Abstract :
Classification is an important technique in the field of Data Mining and Machine Learning. The classifier can predict the class of unknown data based on their given attribute values. In ubiquitous computing environment, a great deal information can be obtained from various sensors. However, with the time going on, new sensor may be recruited. The recruited new sensors may bring new outcomes to the existing attribute. How to handle the new outcomes is a difficult issue. This paper first presents the problem and meanwhile a new method for handling new outcomes is proposed. The old rule is generated from the old data with fewer outcomes and modified and combined with the new data smoothly. In the method, the old rule can improve the performance of classifier constructed only from the new data set. The experiments show that the proposed approach is effective in handling new outcomes.
Keywords :
data mining; learning (artificial intelligence); pattern classification; classification approach; data mining; machine learning; new outcome handling; ubiquitous computing; Classification algorithms; Computer science; Data mining; Decision trees; Machine learning; Recruitment; Sensor phenomena and characterization; Testing; Training data; Ubiquitous computing;
Conference_Titel :
Evolutionary Computation, 2008. CEC 2008. (IEEE World Congress on Computational Intelligence). IEEE Congress on
Conference_Location :
Hong Kong
Print_ISBN :
978-1-4244-1822-0
Electronic_ISBN :
978-1-4244-1823-7
DOI :
10.1109/CEC.2008.4631332
