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Object Classification Method Using Dynamic Random Forests and Genetic Optimization

  • Journal of The Korea Society of Computer and Information
  • Abbr : JKSCI
  • 2016, 21(5), pp.79-89
  • Publisher : The Korean Society Of Computer And Information
  • Research Area : Engineering > Computer Science

김재협 1 김헌기 1 장경현 1 이종민 2 Young Shik Moon 2

1한화탈레스
2한양대학교

Accredited

ABSTRACT

In this paper, we proposed the object classification method using genetic and dynamic random forest consisting of optimal combination of unit tree. The random forest can ensure good generalization performance in combination of large amount of trees by assigning the randomization to the training samples and feature selection, etc. allocated to the decision tree as an ensemble classification model which combines with the unit decision tree based on the bagging. However, the random forest is composed of unit trees randomly, so it can show the excellent classification performance only when the sufficient amounts of trees are combined. There is no quantitative measurement method for the number of trees, and there is no choice but to repeat random tree structure continuously. The proposed algorithm is composed of random forest with a combination of optimal tree while maintaining the generalization performance of random forest. To achieve this, the problem of improving the classification performance was assigned to the optimization problem which found the optimal tree combination. For this end, the genetic algorithm methodology was applied. As a result of experiment, we had found out that the proposed algorithm could improve about 3~5% of classification performance in specific cases like common database and self infrared database compare with the existing random forest. In addition, we had shown that the optimal tree combination was decided at 55~60% level from the maximum trees.

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