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.

Evaluating predictive analytics model performance accuracy for network selection mechanism

Abstract: Predictive analytics has been widely used and adopted in many fields. The idea of anticipating change rather than reacting to change has appealed to many system designers. In this paper, we evaluate the feasibility of applying predictive model into a network selection mechanism to choose most reliable network with higher speed for a communication device such as a modem. The predictive model will attempt to predict best network download speed at a given time of day based on the historical data that we have measured at specific location under studies. This paper also focuses on the accuracy of these predictive models on our sample data, which are measured by calculating its Mean Absolute Error (MAE) and Root Mean Square Error (RMSE) values. Based on the results, a decision tree model outperforms linear regression and M5 models in terms of accuracy. The findings of these studies help us to improve our cognitive network selection algorithm in making decision for best network. Keywords: Network Selection Mechanism, Predictive Models, Linear Regression, Decision Trees, M5

A GA-based Classification Model for Predicting Consumer Choice

Abstract: The purpose of this paper is to develop a new classification method for predicting consumer choice based on genetic algorithm, and to validate Its prediction power over existing methods. To serve this purpose, we propose a hybrid model, and discuss Its methodological characteristics in comparison with other existing classification methods. Also, we conduct a series of experiments employing survey data of consumer choices of MP3 players to assess the prediction power of the model. The results show that the suggested model in this paper is statistically superior to the existing methods such as logistic regression model, artificial neural network model and decision tree model in terms of prediction accuracy. The model is also shown to have an advantage of providing several strategic information of practical use for consumer choice.

Cost-sensitive decision tree construction method based on resource constraint

Abstract: The invention provides a cost-sensitive decision tree construction method based on resource constraint Multiple kinds of identifiers of a training set are divided into two categories at first by using the EP criterion, under the condition of (the formula is as shown in the specification), according to the criterion that a selection attribute cost function gain rate is a target function (the formula is as shown in the specification), the larger the (the formula is as shown in the specification), the batter the attribute feature is, and then the attribute is used as a node or an expansion node For the finally formed decision tree, the decision tree is optimized by using the post shearing algorithm to avoid the excessive fitting problem By adoption of the cost-sensitive decision tree construction method provided by the invention, a better optimized decision tree model with the lowest cost is obtained in a decision tree construction process under the resource constraint

Personal credit risk assessment method based on integrated tree feature extraction and Logistic regression

Abstract: The invention discloses a personal credit risk assessment method based on integrated tree feature extraction and Logistic regression, and belongs to a classification technology for improving personalcredit risk assessment performance, and the method comprises the steps: data collection: obtaining historical credit data of a user as an initial data set; data preprocessing: preprocessing missing values and abnormal values in the initial data set; data division: dividing the data set according to the default and non-default ratios and the ratio of the training set to the test set; feature extraction: an integrated tree model is adopted, the integrated tree model comprises three gradient promotion decision trees of GBDT, XBGost and LightGBM, and realizing feature conversion and extraction; feature fusion: fusing the features extracted from the three gradient promotion decision trees GBDT, XBGost and LightGBM to obtain a new feature set; and model evaluation: establishing a Logistic regression model by adopting the new feature set, and evaluating the classification effect of the model. Experimental results show that the method has excellent personal credit risk assessment performance,and the classification effect of the method is stable. The method can be applied to the field of credit risk assessment of commercial banks and the like, and is an effective risk management tool.