Improving Weighted Fuzzy Decision Tree for Uncertain Data Classification

Abstract

The analytical data about the rainfall pattern, soil structure of the planting crop will partition data by taking full advantage of the incomplete information to achieve better performance. Ignoring uncertain and vague nature of real world will undoubtedly eliminate substantial information. This paper reports the empirical results that provide high return in planting material breeders in agriculture industry through effective policies of decision making. In order to handle the attribute of incomplete information, several fuzzy modeling approach has been proposed, which support the fuzziness at the attribute level. We describe a novel algorithmic framework for this challenge. We first transform the small throughput data into similarity values. Then, we propagate alternate good data and allow decision tree induction to select the best weight for our entropy-based decision tree induction. As a result, we generalize decision algorithms that provide simpler and more understandable classifier to optimally retrieve the information based on user interaction. The proposed method leads to smaller decision tree and as a consequence better test performance in planting material classification.