Abstract

This paper proposes a novel method for generating a decision tree to discriminate polymers accurately with the near-infrared rays spectrum. The polymer discrimination system is needed for recycling plastics, and the near-infrared rays spectrum is useful for rapid and non-destructive discrimination. The former system SESAT, which is based on symbiotic evolution, can generate simple and accurate trees, but is not effective for data that has a lot of attributes like the near-infrared rays spectrum. We design the structure of the partial solution ``sprig'' for sufficient learning, and the fitness function of the whole solution ``decision tree blueprint'' for 2-class discrimination. In addition, we introduce two-step discrimination with the aim of obtaining higher accuracy. In the first step, examples are divided into two groups, one group being easier than the other to discriminate by a tree. In the second step, two trees are generated that discriminate one kind of polymer from the others, for two groups of examples. By doing this, a minority of examples is also discriminated accurately. Based on this method we developed a polymer discrimination system called TS-SEPT. Our experimental results on real data of polymers show that the accuracy of TS-SEPT compares favorably with that of the other systems, the similar system without two-step discrimination, SESAT and C5.0. It emerged that both the method for generating decision trees and two-step discrimination contributed to the improved accuracy.