A SOM combined with KNN for classification task

Silva, Leandro Augusto da; Del-Moral-Hernandez, Emílio

doi:10.1109/ijcnn.2011.6033525

Cited by 21 publications

(15 citation statements)

References 13 publications

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“…K-NN [1], FK-NN [2], EK-NN [11], SOM-KNN [34] and BK-NN [16]), and ENN classifier [12]. The different methods have been programmed and tested with Matlab TM software.…”

Section: Methodsmentioning

confidence: 99%

“…K-NN [1], FK-NN [2], SOM-KNN [34], EK-NN [11] and BK-NN [16]). The basic information about the used data sets are given in Table IV.…”

Section: Methodsmentioning

confidence: 99%

“…Self-Organizing Map (SOM) technique [32] has been successfully applied to reduce the computational burden of K-NN. In [34], a method referred by SOM-KNN was presented, and SOM is employed for the pre-processing of data set. One can define the potential similar patterns region according to the distance between the object and the weighting vectors of SOM, and then the neighborhoods of the object can be found in the given region.…”

Section: Introductionmentioning

confidence: 99%

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Hybrid Classification System for Uncertain Data

Liu

Pan

Dezert

et al. 2017

IEEE Trans. Syst. Man Cybern, Syst.

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Abstract-In classification problem, several different classes may be partially overlapped in their borders. The objects in the border are usually quite difficult to classify. A Hybrid Classification System (HCS) is proposed to adaptively utilize the proper classification method for each object according to the K nearest neighbors, which are found in the weighting vector space obtained by SelfOrganizing Map (SOM) in each class. If the K close weighting vectors (nodes) are all from the same class, it indicates that this object can be correctly classified with high confidence, and the simple hard classification will be adopted to directly classify this object into the corresponding class. If the object likely lies in the border of classes, it implies that this object could be difficult to classify, and the credal classification working with belief functions is recommended. The credal classification allows the object to belong to both singleton classes and sets of classes (meta-class) with different masses of belief, and it is able to well capture the potential imprecision of classification thanks to the meta-class and also reduce the errors. Fuzzy classification is selected for the object close to the border and hard to clearly classify, and it associates the object with different classes by different membership (probability) values. HCS generally takes full advantage of the three classification ways and produces good performance. Moreover, it requires quite low computational burden compared with other K-NN based methods due to the use of SOM. The effectiveness of HCS is demonstrated by several experiments with synthetic and real data sets.

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“…K-NN [1], FK-NN [2], EK-NN [11], SOM-KNN [34] and BK-NN [16]), and ENN classifier [12]. The different methods have been programmed and tested with Matlab TM software.…”

Section: Methodsmentioning

confidence: 99%

“…K-NN [1], FK-NN [2], SOM-KNN [34], EK-NN [11] and BK-NN [16]). The basic information about the used data sets are given in Table IV.…”

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Hybrid Classification System for Uncertain Data

Liu

Pan

Dezert

et al. 2017

IEEE Trans. Syst. Man Cybern, Syst.

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“…kNN classifies object based on the minimal distance to training examples by a majority vote of its neighbors [9]. Specifically, the object is assigned to the most common class among its k-nearest neighbors.…”

Section: K-nearest Neighbormentioning

confidence: 99%

Simultaneous Object Tracking and Classification for Traffic Surveillance

Tuty

Zhang

2014

Advances in Intelligent Systems and Computing

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Object tracking is the problem of estimating the positions of moving objects in image sequences, which is significant in various applications. In traffic surveillance, the tasks of tracking and recognition of moving objects are often inseparable and the accuracy and reliability of a surveillance system can be generally enhanced by integrating them. In this paper, we proposed a traffic surveillance system that features of classification of pedestrian and vehicle types while tracking, which works well in challenging real-word conditions. The object tracking is implemented by the Mean Shift and object classification is implemented with several different classification algorithms including k-nearest neighborhood (kNN), support vector machine (SVM), multi-layer perceptron (MLP), and random forest (RF), with high classification accuracies.

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“…The algorithm strategy for classification comprises three operations: (i) an unlabeled sample is compared to dataset training through a similarity measure; (ii) the labeled objects are sorted in order of similarity to the unlabeled sample; and finally, (iii) the classification occurs giving the unlabeled sample the majority class of the nearest neighbors objects. Because of its simplified algorithm (three basic operations steps), and reduced number of parameters (similarity measure and the k number of nearest neighbor), this instance-based learning algorithm is widely used in the data mining community as a benchmarking algorithm [1–5]. …”

Section: Introductionmentioning

confidence: 99%

Prototype Generation Using Self-Organizing Maps for Informativeness-Based Classifier

Moreira

Silva

2017

Computational Intelligence and Neuroscience

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The k nearest neighbor is one of the most important and simple procedures for data classification task. The kNN, as it is called, requires only two parameters: the number of k and a similarity measure. However, the algorithm has some weaknesses that make it impossible to be used in real problems. Since the algorithm has no model, an exhaustive comparison of the object in classification analysis and all training dataset is necessary. Another weakness is the optimal choice of k parameter when the object analyzed is in an overlap region. To mitigate theses negative aspects, in this work, a hybrid algorithm is proposed which uses the Self-Organizing Maps (SOM) artificial neural network and a classifier that uses similarity measure based on information. Since SOM has the properties of vector quantization, it is used as a Prototype Generation approach to select a reduced training dataset for the classification approach based on the nearest neighbor rule with informativeness measure, named iNN. The SOMiNN combination was exhaustively experimented and the results show that the proposed approach presents important accuracy in databases where the border region does not have the object classes well defined.

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A SOM combined with KNN for classification task

Cited by 21 publications

References 13 publications

Hybrid Classification System for Uncertain Data

Hybrid Classification System for Uncertain Data

Simultaneous Object Tracking and Classification for Traffic Surveillance

Prototype Generation Using Self-Organizing Maps for Informativeness-Based Classifier

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