An overview and comparison of voting methods for pattern recognition

Erp, Merijn van; Vuurpijl, L.G.; Schomaker, Lambert

doi:10.1109/iwfhr.2002.1030908

Cited by 130 publications

(57 citation statements)

References 6 publications

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“…The average accuracy of the voting system was 65.3%. The fact that the voting system improves on the individual classifiers is inline with previous observations [3][5] [18][16] [6].…”

Section: Classificationsupporting

confidence: 79%

Learning Visual Object Categories with Global Descriptors and Local Features

Pereira

Lopes

2009

Progress in Artificial Intelligence

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Abstract. Different types of visual object categories can be found in real-world applications. Some categories are very heterogeneous in terms of local features (broad categories) while others are consistently characterized by some highly distinctive local features (narrow categories). The work described in this paper was motivated by the need to develop representations and categorization mechanisms that can be applied to domains involving different types of categories. A second concern of the paper is that these representations and mechanisms have potential for scaling up to large numbers of categories. The approach is based on combinining global shape descriptors with local features. A new shape representation is proposed. Two additional representations are used, one also capturing the object's shape and another based on sets of highly distinctive local features. Basic classifiers following the nearest-neighbor rule were implemented for each representation. A meta-level classifier, based on a voting strategy, was also implemented. The relevance of each representation and classifier to both broad and narrow categories is evaluated on two datasets with a combined total of 114 categories.

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“…The average accuracy of the voting system was 65.3%. The fact that the voting system improves on the individual classifiers is inline with previous observations [3][5] [18][16] [6].…”

Section: Classificationsupporting

confidence: 79%

Learning Visual Object Categories with Global Descriptors and Local Features

Pereira

Lopes

2009

Progress in Artificial Intelligence

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“…Initially only majority voting schemes were implemented, but in later works more advanced methods were proposed. These take the importance of decisions coming from particular committee members into consideration (van Erp et al, 2002;Kuncheva et al, 2001).…”

Section: Introductionmentioning

confidence: 99%

Combined classifier based on feature space partitioning

Woźniak¹,

Krawczyk²

2012

International Journal of Applied Mathematics and Computer Science

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This paper presents a significant modification to the AdaSS (Adaptive Splitting and Selection) algorithm, which was developed several years ago. The method is based on the simultaneous partitioning of the feature space and an assignment of a compound classifier to each of the subsets. The original version of the algorithm uses a classifier committee and a majority voting rule to arrive at a decision. The proposed modification replaces the fairly simple fusion method with a combined classifier, which makes a decision based on a weighted combination of the discriminant functions of the individual classifiers selected for the committee. The weights mentioned above are dependent not only on the classifier identifier, but also on the class number. The proposed approach is based on the results of previous works, where it was proven that such a combined classifier method could achieve significantly better results than simple voting systems. The proposed modification was evaluated through computer experiments, carried out on diverse benchmark datasets. The results are very promising in that they show that, for most of the datasets, the proposed method outperforms similar techniques based on the clustering and selection approach.

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“…Into this work, new diagnosis methods regarding drill wear characterization and classification by means of a multiclassifier approach is presented. Many authors have adopted approaches based on the combination of classifiers for resolving different problems and by using different combination rules and strategies [4]. The multiclassifier proposed in this study combines the outputs of different data mining techniques based on voting criteria and on the given label distribution, therefore more accurate predictions can be achieved.…”

Section: Introductionmentioning

confidence: 99%

A Multiclassifier Approach for Drill Wear Prediction

Dı́ez

Carrascal

2012

Machine Learning and Data Mining in Pattern Recognition

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Abstract. Classification methods have been widely used during last years in order to predict patterns and trends of interest in data. In present paper, a multiclassifier approach that combines the output of some of the most popular data mining algorithms is shown. The approach is based on voting criteria, by estimating the confidence distributions of each algorithm individually and combining them according to three different methods: confidence voting, weighted voting and majority voting. To illustrate its applicability in a real problem, the drill wear detection in machine-tool sector is addressed. In this study, the accuracy obtained by each isolated classifier is compared with the performance of the multiclassifier when characterizing the patterns of interest involved in the drilling process and predicting the drill wear. Experimental results show that, in general, false positives obtained by the classifiers can be slightly reduced by using the multiclassifier approach.

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An overview and comparison of voting methods for pattern recognition

Cited by 130 publications

References 6 publications

Learning Visual Object Categories with Global Descriptors and Local Features

Learning Visual Object Categories with Global Descriptors and Local Features

Combined classifier based on feature space partitioning

A Multiclassifier Approach for Drill Wear Prediction

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