Methodology Multiclass microarray data classification based on confidence evaluation

Yu, Hualong; Gao, Su-Yan; Qin, Bin; Zhao, Jing

doi:10.4238/2012.may.15.6

Cited by 7 publications

(3 citation statements)

References 26 publications

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“…To our knowledge, no previous work has considered the effect of class imbalance on these coding strategies, although some have indicated that it is, in fact, harmful [33, 34]. In this paper, we proposed two solutions for this problem and used OAA coding as the baseline.…”

Section: Methodsmentioning

confidence: 99%

Recognition of Multiple Imbalanced Cancer Types Based on DNA Microarray Data Using Ensemble Classifiers

Hong

Yang

et al. 2013

BioMed Research International

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DNA microarray technology can measure the activities of tens of thousands of genes simultaneously, which provides an efficient way to diagnose cancer at the molecular level. Although this strategy has attracted significant research attention, most studies neglect an important problem, namely, that most DNA microarray datasets are skewed, which causes traditional learning algorithms to produce inaccurate results. Some studies have considered this problem, yet they merely focus on binary-class problem. In this paper, we dealt with multiclass imbalanced classification problem, as encountered in cancer DNA microarray, by using ensemble learning. We utilized one-against-all coding strategy to transform multiclass to multiple binary classes, each of them carrying out feature subspace, which is an evolving version of random subspace that generates multiple diverse training subsets. Next, we introduced one of two different correction technologies, namely, decision threshold adjustment or random undersampling, into each training subset to alleviate the damage of class imbalance. Specifically, support vector machine was used as base classifier, and a novel voting rule called counter voting was presented for making a final decision. Experimental results on eight skewed multiclass cancer microarray datasets indicate that unlike many traditional classification approaches, our methods are insensitive to class imbalance.

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Section: Methodsmentioning

confidence: 99%

Recognition of Multiple Imbalanced Cancer Types Based on DNA Microarray Data Using Ensemble Classifiers

Hong

Yang

et al. 2013

BioMed Research International

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“…Many research works have investigated bio-inspired computing-based approaches, in conjunction with the wrapper ideology. Hong and Cho [24] and Yu et al [61] used GA for feature selection. Wang et al [58] proposed feature selection using rough sets and particle swarm optimization (PSO).…”

Section: Related Workmentioning

confidence: 99%

An incremental feature selection approach based on scatter matrices for classification of cancer microarray data

Sardana

Agrawal

Kaur

2014

International Journal of Computer Mathematics

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Microarray data are often characterized by high dimension and small sample size. There is a need to reduce its dimension for better classification performance and computational efficiency of the learning model. The minimum redundancy and maximum relevance (mRMR), which is widely explored to reduce the dimension of the data, requires discretization and setting of external parameters. We propose an incremental formulation of the trace of ratio of the scatter matrices to determine a relevant set of genes which does not involve discretization and external parameter setting. It is analytically shown that the proposed incremental formulation is computationally efficient in comparison to its batch formulation. Extensive experiments on 14 well-known available microarray cancer datasets demonstrate that the performance of the proposed method is better in comparison to the well-known mRMR method. Statistical tests also show that the proposed method is significantly better when compared to the mRMR method.

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“…Yu H. L. et. al used a multiclass SVM to classify multi-class microarray data with the aid of classification confidence measure [48]. And there are also some evolutionary algorithms based ensemble systems proposed to deal with this problem [32,33].…”

Section: Lorena and Carvalhomentioning

confidence: 99%

A Hierarchical Ensemble of ECOC for cancer classification based on multi-class microarray data

Liu

Zeng

2016

Information Sciences

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The difficulty of the cancer classification using multi-class microarray datasets lies in that there are only a few samples in each class. To effectively solve such a problem, we propose a hierarchical ensemble strategy, named as Hierarchical Ensemble of Error Correcting Output Codes (HE-ECOC). In this strategy, different feature subsets extracted from a dataset are used as inputs for three data-dependent ECOC algorithms, so as to produce different ECOC coding matrices. The mutual diversity degrees among these coding matrices are then calculated based on two schemes, named as the maximizing local diversity (MLD) and the maximizing global diversity (MGD) schemes. Both schemes can choose diverse coding matrices generated by the same or different ECOC algorithm(s), and the average fusion scheme is used to fuse the outputs of base learners. In the experiments, it is found that both MLD and MGD based HE-ECOC strategies work stably, and outperform individual single ECOC algorithms. In contrast with some ensemble systems, HE-ECOC generates a more robust ensemble system, and achieves better performance in most case. In short, HE-ECOC is a promising solution for the multi-class problem. The matlab code is available upon request.

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Methodology Multiclass microarray data classification based on confidence evaluation

Cited by 7 publications

References 26 publications

Recognition of Multiple Imbalanced Cancer Types Based on DNA Microarray Data Using Ensemble Classifiers

Recognition of Multiple Imbalanced Cancer Types Based on DNA Microarray Data Using Ensemble Classifiers

An incremental feature selection approach based on scatter matrices for classification of cancer microarray data

A Hierarchical Ensemble of ECOC for cancer classification based on multi-class microarray data

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