A new belief-based K-nearest neighbor classification method

Liu, Zhun-ga; Pan, Quan; Dezert, Jean

doi:10.1016/j.patcog.2012.10.001

Cited by 128 publications

(77 citation statements)

References 23 publications

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“…K-Nearest-Neighbors (KNN) [1][2][3][4][5][6][7][8] classifier is an efficient method for classification problems without any need of learning. It works by calculating the distances between a unknown vector (numeral of test) and a set of vectors (numerals of learning base) whose each of them its class is known.…”

Section: Learning-classification Phase 61 the K Nearest Neighborsmentioning

confidence: 99%

“…The second step is the features extraction exploited to extract some efficient features called also the primitives from each image numeral that is presented in form of a matrix which will allow to convert this last to a vector which will facilitate its recognition, in order to realize this phase, we exploited the morphology method. The last phase is the recognition, in this framework we have opted the k-nearest neighbors [1][2][3][4][5][6][7][8] and the multi-layer perceptron [14][15][16][17][18][19][20].…”

Section: Introductionmentioning

confidence: 99%

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A Comparative Study between the K-Nearest Neighbors and the Multi-Layer Perceptron for Cursive Handwritten Arabic Numerals Recognition

Kessab¹,

Daoui²,

Boukhalene³

et al. 2014

IJCA

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In this paper we present a comparison between two supervised classifiers, the first one is a statistic which is the K-Nearest Neighbors (KNN) while the second is a neuronal which is the multi-layer perceptron MLP in the recognition of cursive handwritten Arabic numerals. The recognition process is organized as follows: in the pre-processing of numeral images, we exploited the median filter, the thresholding, the centering and the normalization techniques, in the features extraction we have used the morphology mathematical method. The classification methods include the KNN and the MLP. The simulation results that we obtained demonstrate the MLP is more efficient than the KNN in this recognition. General TermsThe K-Nearest Neighbors (KNN), The multi-layer perceptron (MLP), The mathematical morphology method. KeywordsThe cursive handwritten Arabic numerals: The median filter, the thresholding, the centering and the normalization, techniques, the mathematical morphology method, the KNearest Neighbors (KNN), The multi-layer perceptron (MLP).

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Section: Learning-classification Phase 61 the K Nearest Neighborsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A Comparative Study between the K-Nearest Neighbors and the Multi-Layer Perceptron for Cursive Handwritten Arabic Numerals Recognition

Kessab¹,

Daoui²,

Boukhalene³

et al. 2014

IJCA

View full text Add to dashboard Cite

show abstract

“…In FBK-NN, each labeled sample was assigned with a fuzzy membership to each class according to its neighborhood and the test sample's class label was determined by the K basic belief assignments which were determined from the distances between the object and its K nearest neighbors. A belief theory based k-NN, denoted by the BK-NN classifier was introduced by Liu et al [14]. The author aimed to deal with uncertain data using the meta-class.…”

Section: Introductionmentioning

confidence: 99%

NS-k-NN: Neutrosophic Set-Based k-Nearest Neighbors Classifier

et al. 2017

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k-nearest neighbors (k-NN), which is known to be a simple and efficient approach, is a non-parametric supervised classifier. It aims to determine the class label of an unknown sample by its k-nearest neighbors that are stored in a training set. The k-nearest neighbors are determined based on some distance functions. Although k-NN produces successful results, there have been some extensions for improving its precision. The neutrosophic set (NS) defines three memberships namely T, I and F. T, I, and F shows the truth membership degree, the false membership degree, and the indeterminacy membership degree, respectively. In this paper, the NS memberships are adopted to improve the classification performance of the k-NN classifier. A new straightforward k-NN approach is proposed based on NS theory. It calculates the NS memberships based on a supervised neutrosophic c-means (NCM) algorithm. A final belonging membership U is calculated from the NS triples as U = T + I − F. A similar final voting scheme as given in fuzzy k-NN is considered for class label determination. Extensive experiments are conducted to evaluate the proposed method's performance. To this end, several toy and real-world datasets are used. We further compare the proposed method with k-NN, fuzzy k-NN, and two weighted k-NN schemes. The results are encouraging and the improvement is obvious.

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“…However, the partial imprecision, which is very important in the classification, is not well characterized. We have proposed credal classifiers [23], [24] for complete pattern considering all the possible meta-classes (i.e. the particular disjunctions of several singleton classes) to model the partial imprecise information.…”

Section: Introductionmentioning

confidence: 99%

Adaptive imputation of missing values for incomplete pattern classification

Liu

Pan

Dezert

et al. 2016

Pattern Recognition

Self Cite

163

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In classification of incomplete pattern, the missing values can either play a crucial role in the class determination, or have only little influence (or eventually none) on the classification results according to the context. We propose a credal classification method for incomplete pattern with adaptive imputation of missing values based on belief function theory. At first, we try to classify the object (incomplete pattern) based only on the available attribute values. As underlying principle, we assume that the missing information is not crucial for the classification if a specific class for the object can be found using only the available information. In this case, the object is committed to this particular class. However, if the object cannot be classified without ambiguity, it means that the missing values play a main role for achieving an accurate classification. In this case, the missing values will be imputed based on the K-nearest neighbor (K-NN) and self-organizing map (SOM) techniques, and the edited pattern with the imputation is then classified. The (original or edited) pattern is respectively classified according to each training class, and the classification results represented by basic belief assignments are fused with proper combination rules for making the credal classification. The object is allowed to belong with different masses of belief to the specific classes and meta-classes (which are particular disjunctions of several single classes). The credal classification captures well the uncertainty and imprecision of classification, and reduces effectively the rate of misclassifications thanks to the introduction of meta-classes. The effectiveness of the proposed method with respect to other classical methods is demonstrated based on several experiments using artificial and real data sets.

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A new belief-based K-nearest neighbor classification method

Cited by 128 publications

References 23 publications

A Comparative Study between the K-Nearest Neighbors and the Multi-Layer Perceptron for Cursive Handwritten Arabic Numerals Recognition

A Comparative Study between the K-Nearest Neighbors and the Multi-Layer Perceptron for Cursive Handwritten Arabic Numerals Recognition

NS-k-NN: Neutrosophic Set-Based k-Nearest Neighbors Classifier

Adaptive imputation of missing values for incomplete pattern classification

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