A Study on Exploration of the Recommended Model of Decision Tree to Predict a Hard-to-Measure Mesurement in Anthropometric Survey

인체측정조사에서 측정곤란부위 예측을 위한 의사결정나무 추천 모형 탐지에 관한 연구

Choi, J.H.;Kim, S.K.

  • Published : 2009.10.31


This study aims to explore a recommended model of decision tree to predict a hard-to-measure measurement in anthropometric survey. We carry out an experiment on cross validation study to obtain a recommened model of decision tree. We use three split rules of decision tree, those are CHAID, Exhaustive CHAID, and CART. CART result is the best one in real world data.


Decision tree;k-fold cross validation;CHAID;exhaustive CHAID;CART


  1. 강현철, 한상태, 최종후, 이성건, 김은석, 엄익현, 김미경 (2006). <고객관계관리(CRM)를 위한 데이터마이닝 방법론>, 자유아카데미
  2. 공업진흥청 (1992). KRISS-92-144-IR, <산업제품의 표준치 설정을 위한 국민표준체위 조사보고서>
  3. 박경수 (1993). <인간공학-작업경제학>, 영지문화사
  4. 최종후, 권기만, 김수택 (2002). <신용평점모형>, 세창출판사
  5. 최종후, 소선하 (2005). <사례료 배우는 데이터마이닝>, 자유아카데미
  6. Biggs, D., de Ville. B. and Suen, E. (1991). A Method of choosing multiway partitions for classification and decision trees, Journal of Applied Statistics, 18, 49-62
  7. Breiman, L.. Friedman, J., Olshen, R. and Stone, C. (1984). Classification and Regression Tree, Chapman & Hall/CRC, New York
  8. Han, J. and Kamber, M. (2006). Data Mining: Concepts & Techniques, 2/e, Elsevier Inc, New York
  9. Kass, G. V. (1980). An exploratory technique for investigating large quantities of categorical data, Journal of Applied Statistics, 29, 119-127
  10. Kohavi, R. (1995). A study of cross-validation and bootstrap for accuracy estimation and model selection, Proceedings of the Fourteenth International Joint Conference on Artificial Intelligence, 1137-1143
  11. Quinlan. J. R. (1993). C4.5: Programs for Machine Learning, Morgan-Kaufmann, California