Chandra B, Varghese P Paul
Indian Institute of Technology Delhi, New Delhi110 016, India.
IEEE Trans Syst Man Cybern B Cybern. 2008 Oct;38(5):1294-301. doi: 10.1109/TSMCB.2008.923529.
Traditional decision tree algorithms face the problem of having sharp decision boundaries which are hardly found in any real-life classification problems. A fuzzy supervised learning in Quest (SLIQ) decision tree (FS-DT) algorithm is proposed in this paper. It is aimed at constructing a fuzzy decision boundary instead of a crisp decision boundary. Size of the decision tree constructed is another very important parameter in decision tree algorithms. Large and deeper decision tree results in incomprehensible induction rules. The proposed FS-DT algorithm modifies the SLIQ decision tree algorithm to construct a fuzzy binary decision tree of significantly reduced size. The performance of the FS-DT algorithm is compared with SLIQ using several real-life datasets taken from the UCI Machine Learning Repository. The FS-DT algorithm outperforms its crisp counterpart in terms of classification accuracy. FS-DT also results in more than 70% reduction in size of the decision tree compared to SLIQ.
传统的决策树算法面临着决策边界过于尖锐的问题，而这在任何实际分类问题中都很难出现。本文提出了一种在Quest（SLIQ）决策树中的模糊监督学习（FS-DT）算法。其目的是构建一个模糊决策边界而非清晰的决策边界。构建的决策树大小是决策树算法中的另一个非常重要的参数。大且深的决策树会导致难以理解的归纳规则。所提出的FS-DT算法对SLIQ决策树算法进行了修改，以构建一个大小显著减小的模糊二叉决策树。使用从UCI机器学习库中获取的几个实际数据集，将FS-DT算法的性能与SLIQ进行了比较。在分类准确率方面，FS-DT算法优于其清晰对应算法。与SLIQ相比，FS-DT还使决策树的大小减少了70%以上。
