Performance Comparison of Decision Trees of J48 and Reduced-Error Pruning

Selective Rademacher Penalization and Reduced Error Pruning of Decision Trees

Rademacher penalization is a modern technique for obtaining data-dependent bounds on the generalization error of classifiers. It appears to be limited to relatively simple hypothesis classes because of computational complexity issues. In this paper we, nevertheless, apply Rademacher penalization to the in practice important hypothesis class of unrestricted decision trees by considering the prun...

Incremental Reduced Error Pruning

This paper outlines some problems that may occur with Reduced Error Pruning in relational learning algorithms, most notably efficiency. Thereafter a new method, Incremental Reduced Error Pruning, is proposed that attempts to address all of these problems. Experiments show that in many noisy domains this method is much more efficient than alternative algorithms, along with a slight gain in accur...

a comparison of linguistic and pragmatic knowledge: a case of iranian learners of english

در این تحقیق دانش زبانشناسی و کاربردشناسی زبان آموزان ایرانی در سطح بالای متوسط مقایسه شد. 50 دانش آموز با سابقه آموزشی مشابه از شش آموزشگاه زبان مختلف در دو آزمون دانش زبانشناسی و آزمون دانش گفتار شناسی زبان انگلیسی شرکت کردند که سوالات هر دو تست توسط محقق تهیه شده بود. همچنین در این تحقیق کارایی کتابهای آموزشی زبان در فراهم آوردن درون داد کافی برای زبان آموزان ایرانی به عنوان هدف جانبی تحقیق ...

Pruning Decision Trees and Lists

Machine learning algorithms are techniques that automatically build models describing the structure at the heart of a set of data. Ideally, such models can be used to predict properties of future data points and people can use them to analyze the domain from which the data originates. Decision trees and lists are potentially powerful predictors and embody an explicit representation of the struc...

Pruning Decision Trees and Lists