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.

On the Use of Clustering in Possibilistic Decision Tree Induction

Abstract: This paper presents an extension of a standard decision tree classifier, namely, the C4.5 algorithm. This extension allows the C4.5 algorithm to handle uncertain labeled training data where uncertainty is modeled within the possibility theory framework. The extension mainly concerns the attribute selection measure in which a clustering of possibility distributions of a partition is performed in order to assess the homogeneity of that partition. This paper also provides a comparison with previously proposed possibilistic decision tree approaches.

Comparing performance of decision tree and neural network in predicting myocardial infarction

Abstract: Purpose: Cardiovascular diseases are among the most common diseases in all societies. Using data mining techniques to generate predictive models to identify those at risk for reducing the effects of the disease is very helpful. The main purpose of this study was to predict the risk of myocardial infarction by Decision Tree based on the observed risk factors. Methods: The present work was an analytical study conducted on a database containing 350 records. Data were obtained from patients admitted to Shahid Rajaei specialized cardiovascular hospital, Iran, in 2011. Data were collected using a four-sectioned data collection form. Data analysis was performed using SPSS statistical software version 12 by CRISP methodology. In the modeling section decision tree and Neural Network were used. Results: The results of the data mining showed that the variables of high blood pressure, hyperlipidemia and tobacco smoking were the most critical risk factors of myocardial infarction. The accuracy of the decision tree model on the data was shown to be as 93/4. Conclusion: The best created model was decision tree C5.0. According to the created rules, it can be predicted which patient with new specified features may affected by myocardial infarction.

A Novel k-Parent Flooding Tree for Secure and Reliable Broadcasting in Sensor Networks

Abstract: Securing broadcast communication over sensor networks has become an important research challenge. Intuitively, broadcast communication has two important metrics: reliability and security. Though the reliability metric has drawn sufficient attention in the research community, the security metric has not. In this paper, we address both metrics with an emphasis on the former and address the denial-of-broadcast message attacks (DoBM) in sensor networks. We propose a novel tree-based model called the k-parent flooding tree model (k-FTM) and present algorithms for the construction of k-FTM. The proposed k-FTM is robust against DoBM. It enables the base station to detect DoBM very efficiently, even in the presence of a prudent adversary who focuses on remaining undetected by causing damage below the detection threshold. k-FTM is, to our best knowledge, the first fault-tolerant tree model that is both reliable and secure. Through simulations we confirm that our model achieves detection rates close to that of a static tree and a broadcast reliability close to that of blind flooding.

Application and Analysis of Machine Learning Algorithms for Design of Concrete Mix with Plasticizer and without Plasticizer

Abstract: The objective of this paper is to find an alternative to conventional method of concrete mix design. For finding the alternative, 4 machine learning algorithms viz. multi-variable linear regression, Support Vector Regression, Decision Tree Regression and Artificial Neural Network for designing concrete mix of desired properties. The multi-variable linear regression model is just a simplistic baseline model, support vector regression Artificial Neural Network model were made because past researchers worked heavily on them, Decision tree model was made by authors own intuition. Their results have been compared to find the best algorithm. Finally, we check if the best performing algorithm is accurate enough to replace the convention method. For this, we utilize the concrete mix designs done in lab for various on site designs. The models have been designed for both mixes types – with plasticizer and without plasticizer The paper presents detailed comparison of four models Based on the results obtained from the four models, the best one has been selected based on high accuracy and least computational cost. Each sample had 24 features initially, out of which, most significant features were chosen which were contributing towards prediction of a variable using f regression and p values and models were trained on those selected features. Based on the R squared value, best fitting models were selected among the four algorithms used. From the paper, the author(s) conclude that decision tree regression is best for calculating the amount of ingredients required with R squared values close to 0.8 for most of the models. DTR model is also computationally cheaper than ANN and future works with DTR in mix design is highly recommended in this paper.