Vote-Based Classifier Selection for Biomedical NER Using Genetic Algorithms

Dimililer, Nazife; Varoğlu, Ekrem; Altınçay, Hakan

doi:10.1007/978-3-540-72849-8_26

Cited by 12 publications

(7 citation statements)

References 9 publications

(18 reference statements)

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“…A slightly different voting algorithm was introduced by Dimililer et al . (2007). In their approach, the contribution of each classification model differed among each of the classes.…”

Section: Combining Methodsmentioning

confidence: 99%

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A survey of commonly used ensemble-based classification techniques

Jurek¹,

Bi²,

Wu³

et al. 2013

The Knowledge Engineering Review

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The combination of multiple classifiers, commonly referred to as a classifier ensemble, has previously demonstrated the ability to improve classification accuracy in many application domains. As a result this area has attracted significant amount of research in recent years. The aim of this paper has therefore been to provide a state of the art review of the most well-known ensemble techniques with the main focus on bagging, boosting and stacking and to trace the recent attempts, which have been made to improve their performance. Within this paper, we present and compare an updated view on the different modifications of these techniques, which have specifically aimed to address some of the drawbacks of these methods namely the low diversity problem in bagging or the over-fitting problem in boosting. In addition, we provide a review of different ensemble selection methods based on both static and dynamic approaches. We present some new directions which have been adopted in the area of classifier ensembles from a range of recently published studies. In order to provide a deeper insight into the ensembles themselves a range of existing theoretical studies have been reviewed in the paper.

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“…A slightly different voting algorithm was introduced by Dimililer et al . (2007). In their approach, the contribution of each classification model differed among each of the classes.…”

Section: Combining Methodsmentioning

confidence: 99%

“…The most popular is the one based on either the classifier's accuracy or entropy of the classifier's output. A slightly different voting algorithm was introduced by Dimililer et al (2007). In their approach, the contribution of each classification model differed among each of the classes.…”

Section: Weighting Methodsmentioning

confidence: 99%

A survey of commonly used ensemble-based classification techniques

Jurek¹,

Bi²,

Wu³

et al. 2013

The Knowledge Engineering Review

View full text Add to dashboard Cite

show abstract

“…Figure 1 describes the flowchart of our GA system and Algorithm 1 provides further description of our GA framework. Classifier ensembles can be represented as chromosomes where each bit represents the participation of a classifier in the ensemble as reported in [48]. For a population of size N, C i (where 1 ≤ i ≤ N) are the chromosomes representing classifier ensembles where each chromosome contains M bits such that the first M -1 bits represented by 0 or 1 in location i denotes the absence or presence of a classifier respectively and the last bit Use Mth bit to select the voting method for classifier combination: 0: Minimax Regret, 1: Hurwicz Criterion…”

Section: Proposed Genetic Algorithm Frameworkmentioning

confidence: 99%

Chemical disease relation extraction task using genetic algorithm with two novelvoting methods for classifier subset selection

Onye¹,

Dimililer²,

Akkeleş³

2020

Turk J Elec Eng & Comp Sci

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Biomedical relation extraction is an important preliminary step for knowledge discovery in the biomedical domain. This paper proposes a multiple classifier system (MCS) for the extraction of chemical-induced disease relations. A genetic algorithm (GA) is employed to select classifier ensembles from a pool of base classifiers. Moreover, the voting method used for combining the members of each of the ensembles is also selected during evolution in the GA framework. The performances of the MCSs are determined by the algorithms used for selecting the classifiers, the diversity among the selected classifiers, and the voting method used in the classifier combination. The base classifiers are represented in the form of chromosomes, where each chromosome contains all information on the ensemble it represents: the subset of classifiers voting and the voting method. The chromosomes are evolved using a variety of genetic selection, mating, and mutation techniques in order to find an optimal solution. The aim of the proposed system is to select the subset of classifiers with diverse abilities while maximizing the strengths of the best classifiers in the classifier ensemble for a given voting method. Two main contributions of this work are the evolution of the voting bit as part of the GA and the novel approach of using two different decision-making under uncertainty techniques as voting methods. Furthermore, two different selection algorithms and crossover operators are employed as ways of increasing variations during evolution. We validated our proposed method on nine different experimental settings and they produced good results comparable to the state-of-the-art systems, thereby justifying our approach.

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“…Due to its capacity to learn high dimensional feature sets which are sparse, SVM is one of the most frequently used classifier type in biomedical NER [15,[27][28][29]51]. In this technique, the idea is to map the feature vectors into a higher dimensional space using a kernel function and compute an optimal hyperplane separating positive and negative data with the maximum margin.…”

Section: Individual Classifier Prototypesmentioning

confidence: 99%

Classifier subset selection for biomedical named entity recognition

2008

Self Cite

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Classifier ensembling approach is considered for biomedical named entity recognition task. A vote-based classifier selection scheme having an intermediate level of search complexity between static classifier selection and real-valued and class-dependent weighting approaches is developed. Assuming that the reliability of the predictions of each classifier differs among classes, the proposed approach is based on selection of the classifiers by taking into account their individual votes. A wide set of classifiers, each based on a different set of features and modeling parameter setting are generated for this purpose. A genetic algorithm is developed so as to label the predictions of these classifiers as reliable or not. During testing, the votes that are labeled as being reliable are combined using weighted majority voting. The classifier ensemble formed by the proposed scheme surpasses the full object F-score of the best individual classifier by 2.75% and it is the highest score achieved on the data set considered.

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Vote-Based Classifier Selection for Biomedical NER Using Genetic Algorithms

Cited by 12 publications

References 9 publications

A survey of commonly used ensemble-based classification techniques

A survey of commonly used ensemble-based classification techniques

Chemical disease relation extraction task using genetic algorithm with two novelvoting methods for classifier subset selection

Classifier subset selection for biomedical named entity recognition

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