Decision tree model

About: Decision tree model is a research topic. Over the lifetime, 2256 publications have been published within this topic receiving 38142 citations.

Decision Tree Approach to Discovering Fraud in Leasing Agreements

Abstract: Background: Fraud attempts create large losses for financing subjects in modern economies At the same time, leasing agreements have become more and more popular as a means of financing objects such as machinery and vehicles, but are more vulnerable to fraud attempts Objectives: The goal of the paper is to estimate the usability of the data mining approach in discovering fraud in leasing agreements Methods/Approach: Real-world data from one Croatian leasing firm was used for creating tow models for fraud detection in leasing The decision tree method was used for creating a classification model, and the CHAID algorithm was deployed Results: The decision tree model has indicated that the object of the leasing agreement had the strongest impact on the probability of fraud Conclusions: In order to enhance the probability of the developed model, it would be necessary to develop software that would enable automated, quick and transparent retrieval of data from the system, processing according to the rules and displaying the results in multiple categories

Blockwise Based Detection of Local Defects

Abstract: Print quality is an important criterion for a printer's performance. The detection, classification, and assessment of printing defects can reflect the printer's working status and help to locate mechanical problems inside. To handle all these questions, an efficient algorithm is needed to replace the traditionally visual checking method. In this paper, we focus on pages with local defects including gray spots and solid spots. We propose a coarse-to-fine method to detect local defects in a block-wise manner, and aggregate the blockwise attributes to generate the feature vector of the whole test page for a further ranking task. In the detection part, we first select candidate regions by thresholding a single feature. Then more detailed features of candidate blocks are calculated and sent to a decision tree that is previously trained on our training dataset. The final result is given by the decision tree model to control the false alarm rate while maintaining the required miss rate. Our algorithm is proved to be effective in detecting and classifying local defects compared with previous methods.

Decision tree decomposition-based complex feature selection for text chunking

Abstract: Incorporating a method of feature selection into a classification model often provides a number of advantages. In this paper we propose a new feature selection method based on the discriminative perspective of improving the classification accuracy. The feature selection method is developed for a classification model for text chunking. For effective feature selection, we utilize a decision tree as an intermediate feature space inducer. To select a more compact feature set with less computational load, we organized a partially ordered feature space according to the IGR distribution of features. Experimental results show that: (1) the computational complexity on high-dimensional feature space can be reduced by selecting features based on the decision tree decomposition; (2) the text chunking system using the proposed feature selection can significantly improve the performance compared with a decision tree classifier.