✦ LIBER ✦

Combining classification trees using MLE

✍ Scribed by William D. Shannon; David Banks

Publisher: John Wiley and Sons
Year: 1999
Tongue: English
Weight: 131 KB
Volume: 18
Category: Article
ISSN: 0277-6715
DOI: 10.1002/(sici)1097-0258(19990330)18:6<727::aid-sim61>3.0.co;2-2

No coin nor oath required. For personal study only.

✦ Synopsis

We propose a probability distribution for an equivalence class of classification trees (that is, those that ignore the value of the cutpoints but retain tree structure). This distribution is parameterized by a central tree structure representing the true model, and a precision or concentration coefficient representing the variability around the central tree. We use this distribution to model an observed set of classification trees exhibiting variability in tree structure. We propose the maximum likelihood estimate of the central tree as the best tree to represent the set. This MLE retains the interpretability of a single tree model and has excellent generalizability. We implement an ascent search for the MLE tree structure using a data set of 13 classification trees that predict the presence or absence of cancer based on immune system parameters.

📜 SIMILAR VOLUMES

Building classification trees using the

Building classification trees using the total uncertainty criterion

✍ Joaquín Abellán; Serafín Moral 📂 Article 📅 2003 🏛 John Wiley and Sons 🌐 English ⚖ 90 KB

We present an application of the measure of total uncertainty on convex sets of probability distributions, also called credal sets, to the construction of classification trees. In these classification trees the probabilities of the classes in each one of its leaves is estimated by using the imprecis

Use of classification trees for associat

Use of classification trees for association studies

✍ Heping Zhang; George Bonney 📂 Article 📅 2000 🏛 John Wiley and Sons 🌐 English ⚖ 92 KB

Wavelet neural network classification of

Wavelet neural network classification of EEG signals by using AR model with MLE preprocessing

✍ Abdulhamit Subasi; Ahmet Alkan; Etem Koklukaya; M. Kemal Kiymik 📂 Article 📅 2005 🏛 Elsevier Science 🌐 English ⚖ 268 KB

An evolutionary algorithm for constructi

An evolutionary algorithm for constructing a decision forest: Combining the classification of disjoints decision trees

✍ Lior Rokach 📂 Article 📅 2008 🏛 John Wiley and Sons 🌐 English ⚖ 232 KB

Decision forest is an ensemble classification method that combines multiple decision trees to in a manner that results in more accurate classifications. By combining multiple heterogeneous decision trees, decision forest is effective in mitigating noise that is often prevalent in real-world classifi

Predicting materials properties and beha

Predicting materials properties and behavior using classification and regression trees

✍ Yong Li 📂 Article 📅 2006 🏛 Elsevier Science 🌐 English ⚖ 363 KB

An investigation was conducted to evaluate the effectiveness of a non-parametric statistical methodology of classification and regression tree (CART) [L. Breiman, J.H. Friedman, R.A. Olshen, C.J. Stone, Classification and Regression Trees, Wadsworth Inc., California, 1984] as an alternative to the t

Multiscale texture classification using

Multiscale texture classification using dual-tree complex wavelet transform

✍ Turgay Celik; Tardi Tjahjadi 📂 Article 📅 2009 🏛 Elsevier Science 🌐 English ⚖ 760 KB

This paper presents a multiscale texture classifier that exploits the Gabor-like properties of the dual-tree complex wavelet transform, shift invariance and six directional subbands at each scale, and uses a feature vector comprising of a variance and an entropy at different scales of each of the di