Classifying Cinnamomums using rough sets classifier based on interval-discretization

Abstract

Classification, which is the task of assigning objects to one of several predefined categories, is a pervasive problem that encompasses many diverse applications. Decision tree classifier, which is a simple yet widely used classification technique, employs training data to yield decision rules; moreover, it can create thresholds and then split the list of continuous attributes into descrete intervals for handling continuous attributes (Quinlan in Journal of Artificial Intelligence Research 4:77–90, 1996). Rough set theory (Pawlak in International Journal of Computer and Information Sciences 11:341–356, 1982; International Journal of Man-Machine Studies 20:469–483, 1984; Rough sets: theoretical aspects of reasoning about data. Kluwer, Dordrecht, 1991) has been applied to a wide variety of decision analysis problems for the extraction of rules from databases. This paper proposes a hybrid approach that takes advantage of combining decision tree and rough sets classifier and applies it to plant classification. The introduced approach starts with decision tree classifier (C4.5) as preprocessing technique to make interval-discretization, subsequently, and uses rough set method for extracting rules. The proposed approach aims at finding out classification rules via analyzing lamina attributes (leaf stalk, leaf width, leaf length, length/width ratio) of Cinnamomum, which are gathered and measured by plant specialists in the field of Taiwan. A comparison with the widely used algorithms (e.g., decision tree, multilayer perceptrons, naïve Bayes, and rough sets classifier) is carried out to show numerous advantages of the proposed approach. Finally, employing with test data in which species are unknown, results of classification are approved by consulting the relative plant specialists.