A Scalable Method for Improving the Performance of Classifiers in Multiclass Applications by Pairwise Classifiers and GA

Parvïn, Hamïd; Alizadeh, Hosein; Minaei-Bidgoli, Behrouz; Analoui, Morteza

doi:10.1109/ncm.2008.226

Cited by 18 publications

(14 citation statements)

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“…Neural network ensemble is a special field of classifier ensemble. During recent years, neural network ensemble is becoming a hot spot in machine learning and data mining [14][15][16]. It is also considered in image processing tasks.…”

Section: Related Workmentioning

confidence: 99%

Rule Based Ensembles Using Pair Wise Neural Network Classifiers

Jenghara¹,

Ebrahimpour-Komleh²

2015

IJISA

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Abstract-In value estimation, the inexperienced people's estimation average is good approximation to true value, provided that the answer of these individual are independent. Classifier ensemble is the implementation of mentioned principle in classification tasks that are investigated in two aspects. In the first aspect, feature space is divided into several local regions and each region is assigned with a highly competent classifier and in the second, the base classifiers are applied in parallel and equally experienced in some ways to achieve a group consensus. In this paper combination of two methods are used. An important consideration in classifier combination is that much better results can be achieved if diverse classifiers, rather than similar classifiers, are combined. To achieve diversity in classifiers output, the symmetric pairwise weighted feature space is used and the outputs of trained classifiers over the weighted feature space are combined to inference final result. In this paper MLP classifiers are used as the base classifiers. The Experimental results show that the applied method is promising.

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Section: Related Workmentioning

confidence: 99%

Rule Based Ensembles Using Pair Wise Neural Network Classifiers

Jenghara¹,

Ebrahimpour-Komleh²

2015

IJISA

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“…It means 29 misclassifications have totally occurred in recognition of these two digits (classes). The mostly erroneous pair-classes are respectively (2, 3), (0, 5), (3,4), (1,4), (6,9) and so on according to this matrix. Assume that the ith mostly EPPC is denoted by EPPC i .…”

Section: Proposed Algorithmmentioning

confidence: 99%

“…In practice, there may be problems that one single classifier can not deliver a satisfactory performance [7], [8] and [9]. In such situations, employing ensemble of classifying learners instead of single classifier can lead to a better learning [6].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

A Scalable Heuristic Classifier for Huge Datasets: A Theoretical Approach

Parvïn

Minaei-Bidgoli

Parvin

2011

Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications

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Abstract. This paper proposes a heuristic classifier ensemble to improve the performance of learning in multiclass problems. Although the more accurate classifier leads to a better performance, there is another approach to use many inaccurate classifiers while each one is specialized for a few data in the problem space and using their consensus vote as the classifier. In this paper, some ensembles of classifiers are first created. The classifiers of each of these ensembles jointly work using majority weighting votes. The results of these ensembles are combined to decide the final vote in a weighted manner. Finally the outputs of these ensembles are heuristically aggregated. The proposed framework is evaluated on a very large scale Persian digit handwritten dataset and the experimental results show the effectiveness of the algorithm.

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“…To do this operation it uses confusion matrix to determine which of pairwise classes can be better distinguished. The confusion matrix determines the error distribution on different classes (Parvin et al, 2008d). The entry a ij from confusion matrix determines that how many samples of class c j are classified as class c i .…”

Section: Introductionmentioning

confidence: 99%