K-Nearest Neighbour and Support Vector Machine Hybrid Classification
@article{Hafiz2020KNearestNA, title={K-Nearest Neighbour and Support Vector Machine Hybrid Classification}, author={Abdul Mueed Hafiz}, journal={ArXiv}, year={2020}, volume={abs/2007.00045} }
In this paper, a novel K-Nearest Neighbour and Support Vector Machine hybrid classification technique has been proposed that is simple and robust. It is based on the concept of discriminative nearest neighbourhood classification. The technique consists of using K-Nearest Neighbour Classification for test samples satisfying a proximity condition. The patterns which do not pass the proximity condition are separated. This is followed by sifting the training set for a fixed number of patterns for…
References
SHOWING 1-10 OF 44 REFERENCES
Uniformed two Local Binary Pattern Combined with Neighboring Support Vector Classifier for Classification
- Computer Science
- 2017
This paper develops an efficient and practical method for face classification based on the combination of Uniform Local Binary Patterns (LBPu2) and a the classifier Neighboring Support Vector Classifier (NSVC).
A New Technique for Remote Sensing Image Classification Based on Combinatorial Algorithm of SVM and KNN
- Environmental Science, MathematicsInt. J. Pattern Recognit. Artif. Intell.
- 2018
Remote sensing image classification was performed by combining support vector machine (SVM) and k-nearest neighbor (KNN) and a distance formula is proposed as the measure criterion that considers both luminance and direction of the vectors.
SVM-KNN: Discriminative Nearest Neighbor Classification for Visual Category Recognition
- Computer Science2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'06)
- 2006
This work considers visual category recognition in the framework of measuring similarities, or equivalently perceptual distances, to prototype examples of categories and proposes a hybrid of these two methods which deals naturally with the multiclass setting, has reasonable computational complexity both in training and at run time, and yields excellent results in practice.
Arabic handwritten digit recognition
- Computer ScienceInternational Journal of Document Analysis and Recognition (IJDAR)
- 2008
A fast two-stage classification system for Arabic digits is suggested which achieves as high accuracy as the highest classifier/features combination but with much less recognition time.
Hybridized KNN and SVM for gene expression data classification
- Computer Science
- 2005
It has been demonstrated that the proposed hybridized k-nearest neighbor (KNN) classifiers and SVM (HKNNSVM) is a useful tool for classification and the misclassification rate for the prediction set is reduced with samples pruning used.
Handwritten character recognition using gradient feature and quadratic classifier with multiple discrimination schemes
- Computer ScienceEighth International Conference on Document Analysis and Recognition (ICDAR'05)
- 2005
Several state-of-the-art techniques of handwritten character recognition on this baseline system to improve the recognition accuracy are applied and lead to improvement on the character recognition rate.
LDA/SVM driven nearest neighbor classification
- Computer ScienceIEEE Trans. Neural Networks
- 2003
This work uses local support vector machine learning to estimate an effective metric for producing neighborhoods that are elongated along less discriminant feature dimensions and constricted along most discriminant ones, whereby better classification performance can be achieved.
Gene expression data classification using SVM-KNN classifier
- Computer ScienceProceedings of 2004 International Symposium on Intelligent Multimedia, Video and Speech Processing, 2004.
- 2004
A new classifier that combines support vector machine (SVM) with K nearest neighbor (KNN) for gene expression data classification, taking SVM as a 1NN classifier in which only one representative point is selected for each class.
Mammography classification using modified hybrid SVM-KNN
- Computer Science2017 International Conference on Signal Processing and Communication (ICSPC)
- 2017
A machine learning based mammogram classification using modified hybrid SVM-KNN is proposed to map the feature points to kernel space using kernel and find the K nearest neighbors among the training dataset for a given test data point.