P2-068: Automatic diagnostic classification of dementia with FDG-PET using a spatial decision tree approach

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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A Successive Decision Tree Approach to Mining Remotely Sensed Image Data

Knowledge Discovery and Data Mining ◽

10.4018/978-1-59904-252-7.ch006 ◽

2011 ◽

pp. 98-112

Author(s):

Jianting Zhang ◽

Wieguo Liu ◽

Le Gruenwald

Keyword(s):

Decision Tree ◽

Decision Trees ◽

Classification Accuracy ◽

Decision Rules ◽

Image Data ◽

Remotely Sensed ◽

Fast Speed ◽

Remotely Sensed Image ◽

Tree Approach

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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Decision tree approach for classification of remotely sensed satellite data using open source support

Journal of Earth System Science ◽

10.1007/s12040-013-0339-2 ◽

2013 ◽

Vol 122 (5) ◽

pp. 1237-1247 ◽

Cited By ~ 29

Author(s):

RICHA SHARMA ◽

ANIRUDDHA GHOSH ◽

P K JOSHI

Keyword(s):

Decision Tree ◽

Open Source ◽

Satellite Data ◽

Remotely Sensed ◽

Tree Approach

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Diagnostic Classification of ADHD Versus Control: Support Vector Machine Classification Using Brief Neuropsychological Assessment

Journal of Attention Disorders ◽

10.1177/1087054716649666 ◽

2016 ◽

Vol 24 (11) ◽

pp. 1547-1556 ◽

Cited By ~ 7

Author(s):

Jesse C. Bledsoe ◽

Cao Xiao ◽

Art Chaovalitwongse ◽

Sonya Mehta ◽

Thomas J. Grabowski ◽

...

Keyword(s):

Support Vector Machine ◽

Decision Tree ◽

Sensitivity And Specificity ◽

Neuropsychological Assessment ◽

Feature Selection Method ◽

Diagnostic Classification ◽

Support Vector ◽

Tree Model ◽

Children With Adhd

Objective: Common methods for clinical diagnosis include clinical interview, behavioral questionnaires, and neuropsychological assessment. These methods rely on clinical interpretation and have variable reliability, sensitivity, and specificity. The goal of this study was to evaluate the utility of machine learning in the prediction and classification of children with ADHD–Combined presentation (ADHD-C) using brief neuropsychological measures (d2 Test of Attention, Children with ADHD-C and typically developing control children completed semi-structured clinical interviews and measures of attention/concentration and parents completed symptom severity questionnaires. Method: We used a forward feature selection method to identify the most informative neuropsychological features for support vector machine (SVM) classification and a decision tree model to derive a rule-based model. Results: The SVM model yielded excellent classification accuracy (100%) of individual children with and without ADHD (1.0). Decision tree algorithms identified individuals with and without ADHD-C with 100% sensitivity and specificity. Conclusion:This study observed highly accurate statistical diagnostic classification, at the individual level, in a sample of children with ADHD-C. The findings suggest data-driven behavioral algorithms based on brief neuropsychological data may present an efficient and accurate diagnostic tool for clinicians.

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