Title :
Discovery of Quantified Hierarchical Censored Production Rules (QHCPR) from Large Data Set
Author :
Siddiqui, Tamanna ; Alam, M. Afshar
Author_Institution :
Dept. of Comput. Sci., Jamia Hamdard (Hamdard Univ.), New Delhi, India
Abstract :
In this paper a fusion algorithm is suggested in between two knowledge bases for discovery of QHCPR. HCPR system is capable of handling trade-off between the precision of an inference and its computational efficiency leading to trade-off between the certainty of a conclusion and its specificity. It is an attempt to use Dempster-Shafer interpretation of the HCPRs for the quantification to discover HCPR in the form: Decision If <pre-conditions> Unless <censor conditions> Generality <general info> Specificity<specific info>, [alpha, beta, gamma, delta]. The discovered quantified HCPRs would facilitate quantitative reasoning and learning. Examples are given to demonstrate the performance of proposed approach.
Keywords :
data handling; inference mechanisms; learning (artificial intelligence); Dempster-Shafer interpretation; HCPR system; computational efficiency; fusion algorithm; large data set; quantified hierarchical censored production rules; quantitative learning; quantitative reasoning; Computational efficiency; Computer science; Control systems; Decision making; Inference algorithms; Information processing; Production systems; Redundancy; Tree data structures; Uncertainty; Dempster Shafer theory; HCPR; Knowledge base; fusion algorithm Uncertainty Quantification;
Conference_Titel :
Information Processing, 2009. APCIP 2009. Asia-Pacific Conference on
Conference_Location :
Shenzhen
Print_ISBN :
978-0-7695-3699-6
DOI :
10.1109/APCIP.2009.92
