Boosting, Bagging, and Consensus Based Classification of Multisource Remote Sensing Data

Briem, G.J.; Benediktsson, Jón Atli; Sveinsson, Jóhannes R.

doi:10.1007/3-540-48219-9_28

Cited by 19 publications

(14 citation statements)

References 9 publications

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“…On the other hand, it is easy to see that some of these applications could also benefit from the use of multiple classifiers (e.g., multi-source remote-sensing classification and face recognition [7,30]). Finally, in many applications, a lot of unlabelled data are acquired online during the course of operation of a pattern recognition system.…”

Section: Applications Of Semi-supervised Mcsmentioning

confidence: 99%

Semi-supervised Multiple Classifier Systems: Background and Research Directions

Roli

2005

Multiple Classifier Systems

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Multiple classifier systems have been originally proposed for supervised classification tasks. In the five editions of MCS workshop, most of the papers have dealt with design methods and applications of supervised multiple classifier systems. Recently, the use of multiple classifier systems has been extended to unsupervised classification tasks. Despite its practical relevance, semi-supervised classification has not received much attention. Few works on semi-supervised multiple classifiers appeared in the machine learning literature. This paper’s goal is to review the background results that can be exploited to promote research on semi-supervised multiple classifier systems, and to outline some future research direction

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Section: Applications Of Semi-supervised Mcsmentioning

confidence: 99%

Semi-supervised Multiple Classifier Systems: Background and Research Directions

Roli

2005

Multiple Classifier Systems

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“…The mammalian olfactory system uses a variety of chemical sensors, known as olfactory receptors, combined with automated pattern recognition incorporated in the olfactory bulb and olfactory cortex in the brain [8,9] . No one-receptor type alone identifies a specific odor.…”

Section: The Biological Nosementioning

confidence: 99%

Quality Control of Coffee Using an Electronic Nose System

Shilbayeh¹,

Iskandarani²

2004

American J. of Applied Sciences

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An electronic nose system for quality control of coffee is designed and tested. The system uses the Figaro TGS800 series sensors with an integrated heating element. The testing of the system is carried out using different types of coffee where it is proved successful in classifying the tested coffees and actual discrimination of ingredients into different classes [10] . Database based software is designed to interface the built hardware and to process the electronic nose signals before being classified.

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“…A Bayesian approach has also been used in Consensus based classification of multisource remote sensing data [10,9,19], outperforming conventional multivariate methods for classification. To overcome the problem of the independence assumption (that is unrealistic in most cases), the Behavior-Knowledge Space (BKS) method [56] considers each possible combination of class labels, filling a look-up table using the available data set, but this technique requires a huge volume of training data.…”

Section: Non-generative Ensemblesmentioning

confidence: 99%

Ensembles of Learning Machines

2002

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Abstract. Ensembles of learning machines constitute one of the main current directions in machine learning research, and have been applied to a wide range of real problems. Despite of the absence of an unified theory on ensembles, there are many theoretical reasons for combining multiple learners, and an empirical evidence of the effectiveness of this approach. In this paper we present a brief overview of ensemble methods, explaining the main reasons why they are able to outperform any single classifier within the ensemble, and proposing a taxonomy based on the main ways base classifiers can be generated or combined together.

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Boosting, Bagging, and Consensus Based Classification of Multisource Remote Sensing Data

Cited by 19 publications

References 9 publications

Semi-supervised Multiple Classifier Systems: Background and Research Directions

Semi-supervised Multiple Classifier Systems: Background and Research Directions

Quality Control of Coffee Using an Electronic Nose System

Ensembles of Learning Machines

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