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.

CRDT: Correlation Ratio Based Decision Tree Model for Healthcare Data Mining

Abstract: The phenomenal growth in the healthcare data has inspired us in investigating robust and scalable models for data mining. For classification problems Information Gain(IG) based Decision Tree is one of the popular choices. However, depending upon the nature of the dataset, IG based Decision Tree may not always perform well as it prefers the attribute with more number of distinct values as the splitting attribute. Healthcare datasets generally have many attributes and each attribute generally has many distinct values. In this paper, we have tried to focus on this characteristics of the datasets while analysing the performance of our proposed approach which is a variant of Decision Tree model and uses the concept of Correlation Ratio(CR). Unlike IG based approach, this CR based approach has no biasness towards the attribute with more number of distinct values. We have applied our model on some benchmark healthcare datasets to show the effectiveness of the proposed technique.

The decision tree approach for the choice of freight transport mode : the shippers’ perspective in terms of seaport hinterland connections

Abstract: The project is financed within the framework of the program of the Minister of Science and Higher Education under the name "Regional Excellence Initiative" in the years 2019 - 2022; project number 001/RID/2018/19; the amount of financing PLN 10,684,000.00

Knowledge discovery from gene expression dataset using bagging lasso decision tree

Abstract: Classifying high-dimensional data are a challenging task in data mining. Gene expression data is a type of high-dimensional data that has thousands of features. The study was proposing a method to extract knowledge from high-dimensional gene expression data by selecting features and classifying. Lasso was used for selecting features and the classification and regression tree (CART) algorithm was used to construct the decision tree model. To examine the stability of the lasso decision tree, we performed bootstrap aggregating (Bagging) with 50 replications. The gene expression data used was an ovarian tumor dataset that has 1,545 observations, 10,935 gene features, and binary class. The findings of this research showed that the lasso decision tree could produce an interpretable model that theoretically correct and had an accuracy of 89.32%. Meanwhile, the model obtained from the majority vote gave an accuracy of 90.29% which showed an increase in accuracy of 1% from the single lasso decision tree model. The slightly increasing accuracy shows that the lasso decision tree classifier is stable.

User relationship analysis in campus based on WiFi Hotspots

Abstract: With the rapid development of mobile Internet, the way users access the network becomes diverse, which provide much convenience for us to collect huge amount of users' information. In this paper, we present a model of measuring relationship between two users in campus and build a wireless data analysis system called WiCloud to verify our model. This work has several potential applications such as recommendation, advertisement targeting, and privacy protection. A novel method using decision tree model, which is usually used for decision analysis, is proposed to measure the relationship between two users in wireless network. Experiments results on real datasets validate our ideas and verify the feasibility and efficiency. Experimental results show that the accuracy of the training set is 100%, while the accuracy of the test set is 88.9%.