A Successive Decision Tree Approach to Mining Remotely Sensed Image Data

A Successive Decision Tree Approach to Mining Remotely Sensed Image Data

Jianting Zhang (University of New Mexico, USA), Wieguo Liu (University of Toledo, USA) and Le Gruenwald (University of Oklahoma, USA)
Copyright: © 2007 |Pages: 15
DOI: 10.4018/978-1-59904-252-7.ch006
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Decision trees (DT) has been widely used for training and classification of remotely sensed image data due to its capability to generate human interpretable decision rules and its relatively fast speed in training and classification. This chapter proposes a successive decision tree (SDT) approach where the samples in the ill-classified branches of a previous resulting decision tree are used to construct a successive decision tree. The decision trees are chained together through pointers and used for classification. SDT aims at constructing more interpretable decision trees while attempting to improve classification accuracies. The proposed approach is applied to two real remotely sensed image datasets for evaluations in terms of classification accuracy and interpretability of the resulting decision rules.

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Table of Contents
Philip S. Yu
Xingquan Zhu, Ian Davidson
Xingquan Zhu, Ian Davidson
Chapter 1
Naeem Seliya, Taghi M. Khoshgoftaar
In machine learning the problem of limited data for supervised learning is a challenging problem with practical applications. We address a similar... Sample PDF
Software Quality Modeling with Limited Apriori Defect Data
Chapter 2
Jason H. Moore
Human genetics is an evolving discipline that is being driven by rapid advances in technologies that make it possible to measure enormous quantities... Sample PDF
Genome-Wide Analysis of Epistasis Using Multifactor Dimensionality Reduction: Feature Selection and Construction in the Domain of Human Genetics
Chapter 3
Jose Ma. J. Alvir, Javier Cabrera
Mining clinical trails is becoming an important tool for extracting information that might help design better clinical trials. One important... Sample PDF
Mining Clinical Trial Data
Chapter 4
Jia-Yu Pan, Hyung-Jeong Yang, Christos Faloutsos
Multimedia objects like video clips or captioned images contain data of various modalities such as image, audio, and transcript text. Correlations... Sample PDF
Cross-Modal Correlation Mining Using Graph Algorithms
Chapter 5
Petra Perner
This chapter introduces image mining as a method to discover implicit, previously unknown and potentially useful information from digital image and... Sample PDF
Image Mining for the Construction of Semantic-Inference Rules and for the Development of Automatic Image Diagnosis Systems
Chapter 6
Jianting Zhang, Wieguo Liu, Le Gruenwald
Decision trees (DT) has been widely used for training and classification of remotely sensed image data due to its capability to generate human... Sample PDF
A Successive Decision Tree Approach to Mining Remotely Sensed Image Data
Chapter 7
Tilmann Bruckhaus
This chapter examines the business impact of predictive analytics. It argues that in order to understand the potential business impact of a... Sample PDF
The Business Impact of Predictive Analytics
Chapter 8
Anna Olecka
This chapter will focus on challenges in modeling credit risk for new accounts acquisition process in the credit card industry. First section... Sample PDF
Beyond Classification: Challenges of Data Mining for Credit Scoring
Chapter 9
Elena Irina Neaga
This chapter deals with a roadmap on the bidirectional interaction and support between knowledge discovery (Kd) processes and ontology engineering... Sample PDF
Semantics Enhancing Knowledge Discovery and Ontology Engineering Using Mining Techniques: A Crossover Review
Chapter 10
Amandeep S. Sidhu, Paul J. Kennedy, Simeon Simoff
In some real-world areas, it is important to enrich the data with external background knowledge so as to provide context and to facilitate pattern... Sample PDF
Knowledge Discovery in Biomedical Data Facilitated by Domain Ontologies
Chapter 11
Malcolm J. Beynon
The efficacy of data mining lies in its ability to identify relationships amongst data. This chapter investigates that constraining this efficacy is... Sample PDF
Effective Intelligent Data Mining Using Dempster-Shafer Theory
Chapter 12
Fedja Hadzic, Tharam S. Dillon
Real world datasets are often accompanied with various types of anomalous or exceptional entries which are often referred to as outliers. Detecting... Sample PDF
Outlier Detection Strategy Using the Self-Organizing Map
Chapter 13
Benjamin Griffiths
Predictive accuracy, as an estimation of a classifier’s future performance, has been studied for at least seventy years. With the advent of the... Sample PDF
Re-Sampling Based Data Mining Using Rough Set Theory
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