Randomly selected decision tree for test-cost sensitive learning

Chen Qiu, Liangxiao Jiang, Chaoqun Li

Abstract: Highlights • Test-cost sensitive learning is often desirable in many real-world applications. • We reviewed the related work on test-cost sensitive decision tree learning. • We propose a new test-cost sensitive decision tree learning algorithm. • We conduct a random search to find an appropriate attribute to test at each node. • Experimental results on a large number of datasets validate its effectiveness. Abstract In many real-world applications, decision trees that take account of the cost of acquiring attributes for decision making have been the research focuses. The decision-making process must learn which sequence to perform, and how to build an inexpensive and reliable inductive learning model to accomplish its task. Many previous works in the area of test-cost sensitive decision tree learning have successfully reduced the total test cost, unfortunately also degraded the classification accuracy simultaneously. This paper works on a new idea, i.e., it does not has to reduce the total test cost at the cost of the loss of classification accuracy. For that, we propose a multi-target adaptive attribute selection measure and a simple but effective method for building and testing decision trees. Instead of using a greedy attribute selection measure like many other decision tree learning algorithms, our algorithm uses a random attribute selection measure to find an appropriate attribute to test at each node in the tree. Specifically, we conduct a random search through the whole space of attributes in tree building, and we call the resulting model randomly selected decision tree (RSDT). By this way, RSDT significantly reduces the total test cost, yet at the same time maintains the higher classification accuracy compared to its competitors. The experimental results on 36 UCI datasets validate the effectiveness of our proposed RSDT.