Meta-learning orthographic and contextual models for language independent named entity recognition

Munro, Robert; Ler, Daren; Patrick, Jon

doi:10.3115/1119176.1119207

Cited by 10 publications

(7 citation statements)

References 11 publications

(7 reference statements)

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“…It is rooted in the theory of stochastic discrimination [28], and it has common points with bagging, but instead of sampling instances, it samples subspaces [29]. RSM has been successfully applied to different problems [30], [31]. For instance, in selection based on genetic algorithms, we can evolve the input subspace instead of using RSM, which is a better solution for the stability of -NN rule with respect to sampling (see Section III-B).…”

Section: A Using Standard Instance Selection Methodsmentioning

confidence: 98%

Constructing Ensembles of Classifiers by Means of Weighted Instance Selection

García-Pedrajas

2009

IEEE Trans. Neural Netw.

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In this paper, we approach the problem of constructing ensembles of classifiers from the point of view of instance selection. Instance selection is aimed at obtaining a subset of the instances available for training capable of achieving, at least, the same performance as the whole training set. In this way, instance selection algorithms try to keep the performance of the classifiers while reducing the number of instances in the training set. Meanwhile, boosting methods construct an ensemble of classifiers iteratively focusing each new member on the most difficult instances by means of a biased distribution of the training instances. In this work, we show how these two methodologies can be combined advantageously. We can use instance selection algorithms for boosting using as objective to optimize the training error weighted by the biased distribution of the instances given by the boosting method. Our method can be considered as boosting by instance selection. Instance selection has mostly been developed and used for k -nearest neighbor ( k -NN) classifiers. So, as a first step, our methodology is suited to construct ensembles of k -NN classifiers. Constructing ensembles of classifiers by means of instance selection has the important feature of reducing the space complexity of the final ensemble as only a subset of the instances is selected for each classifier. However, the methodology is not restricted to k-NN classifier. Other classifiers, such as decision trees and support vector machines (SVMs), may also benefit from a smaller training set, as they produce simpler classifiers if an instance selection algorithm is performed before training. In the experimental section, we show that the proposed approach is able to produce better and simpler ensembles than random subspace method (RSM) method for k-NN and standard ensemble methods for C4.5 and SVMs.

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Section: A Using Standard Instance Selection Methodsmentioning

confidence: 98%

Constructing Ensembles of Classifiers by Means of Weighted Instance Selection

García-Pedrajas

2009

IEEE Trans. Neural Netw.

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“…Wu et al (2003) applied both stacking and voting to three learners. Munro et al (2003) employed both voting and bagging for combining classifiers.…”

Section: Learning Techniquesmentioning

confidence: 99%

“…A majority vote of five systems (Chieu and Ng, 2003;Florian et al, 2003;Klein et al, 2003;McCallum and Li, 2003;Whitelaw and Patrick, 2003) performed best on the English development data. Another combination of five systems (Carreras et al, 2003b;Mayfield et al, 2003;McCallum and Li, 2003;Munro et al, 2003;Zhang and Johnson, 2003) obtained the best result for the German development data. We have performed a majority vote with these sets of systems on the related test sets and obtained F β=1 rates of 90.30 for English (14% error reduction compared with the best system) and 74.17 for German (6% error reduction).…”

Section: Performancesmentioning

confidence: 99%

Introduction to the CoNLL-2003 shared task

Sang

Meulder

2003

Proceedings of the Seventh Conference on Natural Language Learning at HLT-NAACL 2003 -

1,646

165

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“…It is rooted on the theory of stochastic discrimination (Kleinberg 2000). The random subspace method has been successfully applied to different problems (Munro et al 2003;Hall et al 2003). …”

Section: Ensemble Construction Using and Artificial Immune Systemmentioning

confidence: 99%

Construction of classifier ensembles by means of artificial immune systems

García-Pedrajas

Fyfe

2007

J Heuristics

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This paper presents the application of Artificial Immune Systems to the design of classifier ensembles. Ensembles of classifiers are a very interesting alternative to single classifiers when facing difficult problems. In general, ensembles are able to achieve better performance in terms of learning and generalisation errors.Several papers have shown that the processes of classifier design and combination must be related in order to obtain better ensembles. Artificial Immune Systems are a recent paradigm based on the immune systems of animals. The features of this new paradigm make it very appropriate for the design of systems where many components must cooperate to solve a given task. The design of classifier ensembles can be considered within such a group of systems, as the cooperation of the individual classifiers is able to improve the performance of the overall system. This paper studies the viability of Artificial Immune Systems when dealing with ensemble design. We construct a population of classifiers that is evolved using an Artificial Immune algorithm. From this population of classifiers several different ensembles can be extracted. These ensembles are favourably compared with ensembles obtained using standard methods in 35 real-world classification problems from the UCI Machine Learning Repository.

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Meta-learning orthographic and contextual models for language independent named entity recognition

Cited by 10 publications

References 11 publications

Constructing Ensembles of Classifiers by Means of Weighted Instance Selection

Constructing Ensembles of Classifiers by Means of Weighted Instance Selection

Introduction to the CoNLL-2003 shared task

Construction of classifier ensembles by means of artificial immune systems

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