대표적인 의사결정나무 알고리즘의 해석력 비교

• 영문명: Interpretability Comparison of Popular Decision Tree Algorithms
• 발행기관: 한국산업경영시스템학회
• 저자명: 홍정식(Jung-Sik Hong) 황근성(Geun-Seong Hwang)
• 간행물 정보: 『산업경영시스템학회지』제44권 제2호, 15~23쪽, 전체 9쪽
• 주제분류: 공학 > 산업공학
• 파일형태: PDF
• 발행일자: 2021.06.30

영문 초록

Most of the open-source decision tree algorithms are based on three splitting criteria (Entropy, Gini Index, and Gain Ratio). Therefore, the advantages and disadvantages of these three popular algorithms need to be studied more thoroughly. Comparisons of the three algorithms were mainly performed with respect to the predictive performance. In this work, we conducted a comparative experiment on the splitting criteria of three decision trees, focusing on their interpretability. Depth, homogeneity, coverage, lift, and stability were used as indicators for measuring interpretability. To measure the stability of decision trees, we present a measure of the stability of the root node and the stability of the dominating rules based on a measure of the similarity of trees. Based on 10 data collected from UCI and Kaggle, we compare the interpretability of DT (Decision Tree) algorithms based on three splitting criteria. The results show that the GR (Gain Ratio) branch-based DT algorithm performs well in terms of lift and homogeneity, while the GINI (Gini Index) and ENT (Entropy) branch-based DT algorithms performs well in terms of coverage. With respect to stability, considering both the similarity of the dominating rule or the similarity of the root node, the DT algorithm according to the ENT splitting criterion shows the best results.

목차

1. 서 론

2. 연구배경

3. DT 알고리즘의 해석력 평가척도

4. 세 개의 DT 알고리즘의 해석력 비교실험

5. 결론 및 추후 연구방향

키워드

Interpretability Comparison Stability Measure Decision Tree