نتایج جستجو برای: ensemble classifiers

تعداد نتایج: 65315  

2004
Christos Dimitrakakis Samy Bengio

Ensemble algorithms can improve the performance of a given learning algorithm through the combination of multiple base classifiers into an ensemble. In this paper, the idea of using an adaptive policy for training and combining the base classifiers is put forward. The effectiveness of this approach for online learning is demonstrated by experimental results on several UCI benchmark databases.

2014
BOLANLE TOLULOPE ABE Tshilidzi Marwala Bolanle Tolulope Abe

not been submitted before for any degree or examination in any other University. Abstract This study presents experimental investigations on supervised ensemble classification for land cover classification. Despite the arrays of classifiers available in machine learning to create an ensemble, knowing and understanding the correct classifier to use for a particular dataset remains a major challe...

2011
B. Kalpana

Given a choice of classifiers each performing differently on different datasets the best option to assume is an ensemble of classifiers. An ensemble uses a single learning algorithm, whereas in this paper we propose a two stage stacking method with decision tree c4.5 as meta classifier. The base classifiers are Naïve Bayes, KNN and C4.5 tree. The decision tree learns from the classification out...

Journal: :Inf. Sci. 2009
Eitan Menahem Lior Rokach Yuval Elovici

The idea of ensemble methodology is to build a predictive model by integrating multiple models. It is well-known that ensemble methods can be used for improving prediction performance. Researchers from various disciplines such as statistics, machine learning, pattern recognition, and data mining have considered the use of ensemble methodology. Stacking is a general ensemble method in which a nu...

2001
Padraig Cunningham Gabriele Zenobi

Ensembles of classifiers will produce lower errors than the member classifiers if there is diversity in the ensemble. One means of producing this diversity in nearest neighbour classifiers is to base the member classifiers on different feature subsets. In this paper we show four examples where this is the case. This has implications for the practice of feature subset selection (an important iss...

2004
Amit Mandvikar Huan Liu Hiroshi Motoda

Generic ensemble methods can achieve excellent learning performance, but are not good candidates for active learning because of their different design purposes. We investigate how to use diversity of the member classifiers of an ensemble for efficient active learning. We empirically show, using benchmark data sets, that (1) to achieve a good (stable) ensemble, the number of classifiers needed i...

Journal: :CoRR 2017
Dhruv Kumar Mahajan Vivek Gupta S. Sathiya Keerthi Sellamanickam Sundararajan Shravan Narayanamurthy Rahul Kidambi

For many applications, an ensemble of base classifiers is an effective solution. The tuning of its parameters (number of classifiers, amount of data on which each classifier is to be trained on, etc.) requires G, the generalization error of a given ensemble. The efficient estimation of G is the focus of this paper. The key idea is to approximate the variance of the class scores/probabilities of...

Journal: :Algorithms 2009
Dmitry Zinovev Daniela Stan Raicu Jacob D. Furst Samuel G. Armato

This paper uses an ensemble of classifiers and active learning strategies to predict radiologists’ assessment of the nodules of the Lung Image Database Consortium (LIDC). In particular, the paper presents machine learning classifiers that model agreement among ratings in seven semantic characteristics: spiculation, lobulation, texture, sphericity, margin, subtlety, and malignancy. The ensemble ...

2005
Luiz Eduardo Soares de Oliveira Marisa E. Morita Robert Sabourin Flávio Bortolozzi

Feature selection for ensembles has shown to be an effective strategy for ensemble creation due to its ability of producing good subsets of features, which make the classifiers of the ensemble disagree on difficult cases. In this paper we present an ensemble feature selection approach based on a hierarchical multi-objective genetic algorithm. The algorithm operates in two levels. Firstly, it pe...

Journal: :J. Network and Computer Applications 2007
Donn Morrison Liyanage C. De Silva

Affect or emotion classification from speech has much to benefit from ensemble classification methods. In this paper we apply a simple voting mechanism to an ensemble of classifiers and attain a modest performance increase compared to the individual classifiers. A natural emotional speech database was compiled from 11 speakers. Listener-judges were used to validate the emotional content of the ...

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