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.

An Algorithm for Constructing Decision Tree Based on Variable Precision Rough Set Model

Abstract: This paper presents a new approach for constructing decision trees based on variable precision rough set model. The presented approach is aimed at handling uncertain information during the process of inducing decision trees and generalizes the rough set based approach to decision tree construction by allowing some extent misclassification when classifying objects. In the paper, variable precision weighted mean precision are introduced. The new algorithm effectively overcomes the influence of the noise data in structuring decision tree, reduces the complexity of decision tree and strengthens its extensive ability.

Decision Tree Technique and its Current Research

Abstract: An introduction and overview of decision tree induction technique is provided. The basic methods of the current technique of decision tree pruning are discussed. The key ways of simplifying decision trees are also discussed. The research directions of decision tree technique and its engineering applications are reviewed, such as integration with other techniques, better methods to construct and simplify, the impact of data size and quality on decision tree performances, how to build decision trees in an uncertain environment, the trade-off between time complexity and accuracy, software implementation of decision tree induction, etc. The future picture for decision tree techniques is given.

Prediction of Flight Time Deviation for Lithuanian Airports Using Supervised Machine Learning Model.

Abstract: In the paper, the flight time deviation of Lithuania airports has been analyzed. The supervised machine learning model has been implemented to predict the interval of time delay deviation of new flights. The analysis has been made using seven algorithms: probabilistic neural network, multilayer perceptron, decision trees, random forest, tree ensemble, gradient boosted trees, and support vector machines. To find the best parameters which give the highest accuracy for each algorithm, the grid search has been used. To evaluate the quality of each algorithm, the five measures have been calculated: sensitivity/recall, precision, specificity, F-measure, and accuracy. All experimental investigation has been made using the newly collected dataset from Lithuania airports and weather information on departure/landing time. The departure flights and arrival flights have been investigated separately. To balance the dataset, the SMOTE technique is used. The research results showed that the highest accuracy is obtained using the tree model classifiers and the best algorithm of this type to predict is gradient boosted trees.

Arthur-Merlin Games in Boolean Decision Trees

Abstract: It is well known that probabilistic boolean decision trees cannot be much more powerful than deterministic ones (N. Nisan, SIAM J. Comput.20, No. 6 (1991), 999?1007). Motivated by a question if randomization can significantly speed up a nondeterministic computation via a boolean decision tree, we address structural properties of Arthur?Merlin games in this model and prove some lower bounds. We consider two cases of interest, the first when the length of communication between the players is limited and the second, if it is not. While in the first case we can carry over the relations between the corresponding Turing complexity classes, in the second case we observe in contrast with Turing complexity that a one-round Merlin?Arthur protocol is as powerful as a general interactive proof system and, in particular, can simulate a one-round Arthur?Merlin protocol. Moreover, we show that sometimes a Merlin?Arthur protocol can be more efficient than an Arthur?Merlin protocol and than a Merlin?Arthur protocol with limited communication. This is the case for a boolean function whose set of zeroes is a code with high minimum distance and a natural uniformity condition. Such functions provide an example when the Merlin?Arthur complexity is 1 with one-sided error ??(23, 1), but at the same time the nondeterministic decision tree complexity is ?(n). The latter should be contrasted with another fact we prove. Namely, if a function has Merlin?Arthur complexity 1 with one-sided error probability ??(0, 23, then its nondeterministic complexity is bounded by a constant. Other results of the paper include connections with the block sensitivity and related combinatorial properties of a boolean function.

Optimal Compensation Rate Appraisal and Selection Based on Data Mining Method

Abstract: By using data mining method in this paper, we try to make more effective market segmentation and find out the optimal price of settlement in financial companies. Through the assessment of the customer value by using BP neural network, the customers can be classified scientifically and rationally. At the same time, we can also adopt different marketing strategies to different customers and improve the customer relationship management. At the same time, by using decision tree model and enhanced ID3 algorithm, we find out settlement of car insurance is mainly influenced by the driving experience and the use process, and then we make a further analysis on these influence factors.