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What is Decision Trees

Encyclopedia of Artificial Intelligence
Decision tree is a classifier in the form of a tree structure, where each node is either a leaf node or a decision node. A decision tree can be used to classify an instance by starting at the root of the tree and moving through it until a leaf node, which provides the classification of the instance. A well known and frequently used algorithm of decision tree over the years is C4.5
Published in Chapter:
Improving the Naïve Bayes Classifier
Liwei Fan (National University of Singapore, Singapore) and Kim Leng Poh (National University of Singapore, Singapore)
Copyright: © 2009 |Pages: 5
DOI: 10.4018/978-1-59904-849-9.ch130
A Bayesian Network (BN) takes a relationship between graphs and probability distributions. In the past, BN was mainly used for knowledge representation and reasoning. Recent years have seen numerous successful applications of BN in classification, among which the Naïve Bayes classifier was found to be surprisingly effective in spite of its simple mechanism (Langley, Iba & Thompson, 1992). It is built upon the strong assumption that different attributes are independent with each other. Despite of its many advantages, a major limitation of using the Naïve Bayes classifier is that the real-world data may not always satisfy the independence assumption among attributes. This strong assumption could make the prediction accuracy of the Naïve Bayes classifier highly sensitive to the correlated attributes. To overcome the limitation, many approaches have been developed to improve the performance of the Naïve Bayes classifier. This article gives a brief introduction to the approaches which attempt to relax the independence assumption among attributes or use certain pre-processing procedures to make the attributes as independent with each other as possible. Previous theoretical and empirical results have shown that the performance of the Naïve Bayes classifier can be improved significantly by using these approaches, while the computational complexity will also increase to a certain extent.
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Deep-Auto Encoders for Detecting Credit Card Fraud
It is a supervised learning model. It learns the decision rules from the training data to classify an unknown test example.
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Holistic View on Detecting DDoS Attacks Using Machine Learning
Supervised method used for classification and regression problems, that simulates a tree diagram and in which the branches represent choices with associated costs, results, or probabilities.
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Detecting Bank Financial Fraud in South Africa Using a Logistic Model Tree
These are classification and regression supervised learning procedures based on a non-parametric approach.
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Visualization of Predictive Modeling for Big Data Using Various Approaches When There Are Rare Events at Differing Levels
A predictive model comprised of multiple steps at which each step, the target variable with a value of 1 is assigned to one branch while the target variable with a value of 0 is assigned to the other branch based on the value of another variable in the data set.
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Emulating Subjective Criteria in Corpus Validation
Classifier consisting on an arboreal structure. A test sample is classified by evaluating it in each node, starting at the top one and choosing a specific branch depending on this evaluation. The classification of the sample is the class assigned in the bottom node.
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Neural Networks for Automobile Insurance Pricing
Tree-shaped structures that represent sets of decisions. These decisions generate rules for the classification of a dataset.
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Principles on Symbolic Data Analysis
A method of finding rules or (rule induction) which divide the data into subgroups which are as similar as possible with regard to a target variable (variable that we want to explain).
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Decision Support Approach for Assessing of Rail Transport: Methods Based on AI and Machine Learning
A decision tree is made up of nodes corresponding to the attributes of the selected objects and of branches characterizing the alternative values of these attributes. The leaves of the tree represent the sets of objects of the same class. The construction of decision trees is a top-down generalization approach. The QUINLAN method (ID3 algorithms) consists of successively testing each attribute to know which one to use first so as to optimize the information gain, that is, the attribute which best allows to distinguish the examples of different classes.
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Customer Relationship Management and Data Mining: A Classification Decision Tree to Predict Customer Purchasing Behavior in Global Market
As a hierarchical collection of rules that describe how to divide a large collection of records into successively smaller group of records. With each successive division, the member of the resulting segment became more and more similar to one another with respect to the dependent variable.
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Computational Models for the Analysis of Modern Biological Data
Predictive models that map the observations about an event to infer about its target value.
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