Prediction of Missing Associations Using Rough Computing and Bayesian Classification
D. P. Acharjya, Debasrita Roy, Md Abdur Rahaman · International Journal of Intelligent Systems and Applications · 2012
Information technology revolution has brought a radical change in the way data are collected or generated for ease of decision making.It is generally observed that the data has not been consistently collected.The huge amount of data has no relevance unless it provides certain useful information.On ly by unlocking the hidden data we can not use it to gain insight into customers, markets, and even to setup a new business.Therefore, the absence of associations in the attribute values may have informat ion to predict the decision for our own business or to setup a new business.Based on decision theory, in the past many mathematical models such as naï ve Bayes structure, human composed network structure, Bayesian network modeling etc. were developed.But, many such models have failed to include important aspects of classification.Therefore, an effo rt has been made to process inconsistencies in data being considered by Pawlak with the introduction of rough set theory.In this paper, we use two processes such as pre process and post process to predict the output values for the missing associations in the attribute values.In pre process we use rough computing, whereas in post process we use Bayesian classification to exp lore the output value for the missing associations and to get better knowledge affecting the decision making.