Title :
Assessment by belief
Author :
Miura, Takao ; Shioya, Isamu
Author_Institution :
Dept. of Electr. & Electron. Eng., Hosei Univ., Tokyo, Japan
Abstract :
We discuss how to post-evaluate inductive classification based on user belief. Although we could learn classification rules inductively by means of decision tree generation, we wonder whether it is consistent with our utilization or not. In the investigation we discuss how to obtain assessment of learning results by verifying belief. Our idea is based on a decision tree with hierarchy to class and attributes; to each attribute we assume taxonomy on the domain in addition to class hierarchy. Then, given a firm belief (such as regulation and top executive policy), we check whether the trees satisfy it and we can see the usefulness of the trees
Keywords :
belief maintenance; computability; data mining; database theory; decision trees; learning (artificial intelligence); pattern classification; very large databases; DTH; assessment by belief; class hierarchy; classification rule learning; data mining; decision tree generation; decision tree with hierarchy; firm belief; inductive classification; knowledge discovery; machine learning; regulation; top executive policy; user belief; Classification tree analysis; Databases; Decision trees; Frequency; Knowledge acquisition; Machine learning; Polynomials; Production; Taxonomy; Training data;
Conference_Titel :
Database Conference, 2001. ADC 2001. Proceedings. 12th Australasian
Conference_Location :
Gold Coast, Qld.
Print_ISBN :
0-7695-0966-5
DOI :
10.1109/ADC.2001.904474
