Ensemble-based classifiers for cancer classification using human tumor microarray data

Margoosian, Argin; Abouei, Jamshid

doi:10.1109/iraniancee.2013.6599553

Cited by 5 publications

(1 citation statement)

References 19 publications

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“…Multiple classifier combination has been widely used in many application domains such as: speech recognition [3], human emotion recognition [4], video classification [5], face recognition [6], email classification [7], cancer classification [8], plant leaf identification [9], concept drift identification [10] and sukuk rating prediction [11]. Multiple classifier combination has been very useful in enhancing the performance of classification.…”

Section: Introductionmentioning

confidence: 99%

Ant System and Weighted Voting Method for Multiple Classifier Systems

Abdullah

Ku-Mahamud

2018

IJECE

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Combining multiple classifiers is considered as a general solution for classification tasks. However, there are two problems in combining multiple classifiers: constructing a diverse classifier ensemble; and, constructing an appropriate combiner. In this study, an improved multiple classifier combination scheme is propose. A diverse classifier ensemble is constructed by training them with different feature set partitions. The ant system-based algorithm is used to form the optimal feature set partitions. Weighted voting is used to combine the classifiers’ outputs by considering the strength of the classifiers prior to voting. Experiments were carried out using k-NN ensembles on benchmark datasets from the University of California, Irvine, to evaluate the credibility of the proposed method. Experimental results showed that the proposed method has successfully constructed better k-NN ensembles. Further more the proposed method can be used to develop other multiple classifier systems.

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