LNCS 2810 Numerical Attributes in Decision Trees A Hierarchical Approach 1st Edition by Fernando Berzal, Juan Carlos Cubero, Nicolás Marín, Daniel Sánchez  ISBN 3540452311 9783540452317

LNCS 2810 – Numerical Attributes in Decision Trees: A Hierarchical Approach 1st Edition by Fernando Berzal, Juan-Carlos Cubero, Nicolás Marín, Daniel Sánchez  – Ebook PDF Instant Download/Delivery. 3540452311, 9783540452317

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Product details:

ISBN 10: 3540452311

ISBN 13: 9783540452317

Author:  Fernando Berzal, Juan-Carlos Cubero, Nicolás Marín, Daniel Sánchez  

LNCS 2810 – Numerical Attributes in Decision Trees: A Hierarchical Approach 1st Edition: 

Decision trees are probably the most popular and commonly-used classification model. They are recursively built following a top-down approach (from general concepts to particular examples) by repeated splits of the training dataset. When this dataset contains numerical attributes, binary splits are usually performed by choosing the threshold value which minimizes the impurity measure used as splitting criterion (e.g. C4.5 gain ratio criterion or CART Gini’s index). In this paper we propose the use of multi-way splits for continuous attributes in order to reduce the tree complexity without decreasing classification accuracy. This can be done by intertwining a hierarchical clustering algorithm with the usual greedy decision tree learning.

 

LNCS 2810 – Numerical Attributes in Decision Trees: A Hierarchical Approach 1st Edition Table of contents:

1. Introduction

2. Splitting criteria

3. Binary splits for numerical attributes

4. Multi-way splits for numerical attributes

4.1. Classical value clustering

4.2. Discretization techniques

4.3. An alternative approach: taking context into account

4.3.1. An example

4.3.2. Measuring similarity

4.3.3. Interleaving the hierarchical clustering algorithm with the TDIDT evaluation process

5. Experimental results

6. Conclusions

 

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