Pruning Method of Belief Decision Trees

نویسندگان

  • Salsabil Trabelsi
  • Zied Elouedi
  • Khaled Mellouli
چکیده

The belief decision tree (BDT) approach is a decision tree in an uncertain environment where the uncertainty is represented through the Transferable Belief Model (TBM), one interpretation of the belief function theory. The uncertainty can appear either in the actual class of training objects or attribute values of objects to classify. In this paper, we develop a post-pruning method of belief decision trees in order to reduce size and improve classification accuracy on unseen cases. The pruning of decision tree has a considerable intention in the areas of machine learning.

برای دانلود رایگان متن کامل این مقاله و بیش از 32 میلیون مقاله دیگر ابتدا ثبت نام کنید

ثبت نام

اگر عضو سایت هستید لطفا وارد حساب کاربری خود شوید

منابع مشابه

A New Pruning Method for Solving Decision Trees and Game Trees

The main goal of this paper is to describe a new pruning method for solving decision trees and game trees. The pruning method for decision trees suggests a slight variant of decision trees that we call scenario trees. In scenario trees, we do not need a conditional probability for each edge emanating from a chance node. Instead, we require a joint probability for each path from the root node to...

متن کامل

A Pre-Pruning Method in Belief Decision Trees

The belief decision tree approach is a decision tree method adapted in order to handle uncertainty about the actual class of the objects in the training set. The uncertainty is represented by the Transferable Belief Model (TBM). We present two methods to build the tree. In order to reduce the size and the complexity of the induced tree, we present a pre-pruning tool related to the stopping crit...

متن کامل

Pruning Regression Trees with MDL

Pruning is a method for reducing the error and complexity of induced trees. There are several approaches to pruning decision trees, while regression trees have attracted less attention. We propose a method for pruning regression trees based on the sound foundations of the MDL principle. We develop coding schemes for various constructs and models in the leaves and empirically test the new method...

متن کامل

Pruning belief decision tree methods in averaging and conjunctive approaches

The belief decision tree (BDT) approach is a decision tree in an uncertain environment where the uncertainty is represented through the Transferable Belief Model (TBM), one interpretation of the belief function theory. The uncertainty can appear either in the actual class of training objects or attribute values of objects to classify. From the procedures of building BDT, we mention the averagin...

متن کامل

ذخیره در منابع من


  با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید

عنوان ژورنال:

دوره   شماره 

صفحات  -

تاریخ انتشار 2006