Two Step Decision Tree for Polymer Discrimination

Abstract

SummaryThis 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.

Modifiable Attribute Cell Problem and Solution Method for Population Synthesis in Land Use Microsimulation

“…This results in a fast, efficient search and prevents convergence to suboptimal solutions. Its effectiveness had been demonstrated not only in neural networks but also in inductive logic programming, the generation of decision trees, and so on (13)(14)(15)(16).…”