A semilabeled-sample-driven bagging technique for Ill-posed classification problems

doi:10.1109/LGRS.2004.841478

Return

A semilabeled-sample-driven bagging technique for Ill-posed classification problems

2005-01-01

PRE

Mingmin Chi

Lorenzo Bruzzone

DOI：10.1109/LGRS.2004.841478

Original

Original request for help

Save

Abstract

En 中文

In this letter, a semilabeled-sample-driven bootstrap aggregating (bagging) technique based on a co-inference (inductive and transductive) framework is proposed for addressing ill-posed classification problems. The novelties of the proposed technique lie in: 1) the definition of a general classification strategy for ill-posed problems by the joint use of training and semilabeled samples (i.e., original unlabeled samples labeled by the classification process); and 2) the design of an effective bagging method (driven by semilabeled samples) for a proper exploitation of different classifiers based on bootstrapped hybrid training sets. Although the proposed technique is general and can be applied to any classification algorithm, in this letter multilayer perceptron neural networks (MLPs) are used to develop the basic classifier of the proposed architecture. In this context, a novel cost function for the training of MLPs is defined, which properly considers the contribution of semilabeled samples in the learning of each member of the ensemble. The experimental results, which are obtained on different ill-posed classification problems, confirm the effectiveness of the proposed technique.

Keywords:

bagging

ill-posed classification problems

multiple classifier systems

remote sensing images

semilabeled samples

supervised classification

AI Summary

Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.