A local mean-based nonparametric classifier

Mitani, Yoshihiro; Hamamoto, Yoshihiko

doi:10.1016/j.patrec.2005.12.016

Cited by 177 publications

(95 citation statements)

References 4 publications

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“…To address the various issues over the choice of appropriate number of nearest neighbors with improvement in the classification accuracy in machine learning and pattern recognition domain was presented in [15][16][17]21]. While issues related to the size of training sample set and its impact on classification performance using nearest neighbor rule was presented in [18].…”

Section: Related Workmentioning

confidence: 99%

Retracted: A Novel Voting Mathematical Rule Classification for Image Recognition

Abbasi

Shahriari

Nemati

2016

Computational Science and Its Applications – ICCSA 2016

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Abstract. In machine learning, the accuracy of the system depends upon classification result. Classification accuracy plays an imperative role in various domains. Non-parametric classifier like K-Nearest Neighbor (KNN) is the most widely used classifier for pattern analysis. Besides its easiness, simplicity and effectiveness characteristics, the main problem associated with KNN classifier is the selection of number of nearest neighbors i.e. 'k' for computation. At present it is hard to find the optimal value of 'k' using any statistical algorithm, which gives perfect accuracy in terms of low misclassification error rate.Motivated by prescribed problem, a new sample space reduction weighted voting mathematical rule (AVNM) is proposed for classification in machine learning. Proposed AVNM rule is also non-parametric in nature like KNN. AVNM uses the weighted voting mechanism with sample space reduction to learn and examine the predicted class label for unidentified sample. AVNM is free from any initial selection of predefined variable and neighbor selection as found in KNN algorithm. Proposed classifier also reduces the effect of outliers.To verify the performance of the proposed AVNM classifier, experiments are made on 10 standard dataset taken from UCI database and one manually created dataset. Experimental result shows that the proposed AVNM rule outperforms KNN classifier and its variants. Experimentation results based on confusion matrix accuracy parameter proves higher accuracy value with AVNM rule.Proposed AVNM rule is based on sample space reduction mechanism for identification of optimal number of nearest neighbor selections. AVNM results in better classification accuracy and minimum error rate as compared with state-of-art algorithm, KNN and its variants. Proposed rule automates the selection of nearest neighbor selection and improves classification rate for UCI dataset and manual created dataset.

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Section: Related Workmentioning

confidence: 99%

Retracted: A Novel Voting Mathematical Rule Classification for Image Recognition

Abbasi

Shahriari

Nemati

2016

Computational Science and Its Applications – ICCSA 2016

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“…In [44], Wen et al presented a new algorithm called Relative Local Mean Classifier (RLMC) for dealing with sparse high dimensional data. They transformed the Local Mean Classifier (LMC) [45], using Euclidean distance, in accordance with the human visual perception ability for better classification. This algorithm was further improved by considering the densities of classes in [46].…”

Section: Related Workmentioning

confidence: 99%

Stimuli-Magnitude-Adaptive Sample Selection for Data-Driven Haptic Modeling

Abdulali

Hassan

Jeon

2016

Entropy

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Data-driven haptic modeling is an emerging technique where contact dynamics are simulated and interpolated based on a generic input-output matching model identified by data sensed from interaction with target physical objects. In data-driven modeling, selecting representative samples from a large set of data in a way that they can efficiently and accurately describe the whole dataset has been a long standing problem. This paper presents a new algorithm for the sample selection where the variances of output are observed for selecting representative input-output samples in order to ensure the quality of output prediction. The main idea is that representative pairs of input-output are chosen so that the ratio of the standard deviation to the mean of the corresponding output group does not exceed an application-dependent threshold. This output-and standard deviation-based sample selection is very effective in applications where the variance or relative error of the output should be kept within a certain threshold. This threshold is used for partitioning the input space using Binary Space Partitioning-tree (BSP-tree) and k-means algorithms. We apply the new approach to data-driven haptic modeling scenario where the relative error of the output prediction result should be less than a perceptual threshold. For evaluation, the proposed algorithm is compared to two state-of-the-art sample selection algorithms for regression tasks. Four kinds of haptic related behavior-force datasets are tested. The results showed that the proposed algorithm outperformed the others in terms of output-approximation quality and computational complexity.

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“…However, the distance between images suffers sensitivity to geometric transformations (shifts, rotations and others) due to high-dimensionality of vectors. For overcoming this difficulty, it is useful to apply local mean classifier [8] because mean vectors help to reduce influence of geometric transformations. In fact, local mean classifier outperformed the nearest neighbor rule in image classification such as handwritten character recognition [9].…”

Section: Proposed Color Image Classificationmentioning

confidence: 99%

Color Image Classification Using Block Matching and Learning

Kondo

Hotta

2009

IEICE Trans. Inf. & Syst.

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SUMMARYIn this paper, we propose block matching and learning for color image classification. In our method, training images are partitioned into small blocks. Given a test image, it is also partitioned into small blocks, and mean-blocks corresponding to each test block are calculated with neighbor training blocks. Our method classifies a test image into the class that has the shortest total sum of distances between mean blocks and test ones. We also propose a learning method for reducing memory requirement. Experimental results show that our classification outperforms other classifiers such as support vector machine with bag of keypoints. key words: color image, block matching, learning vector quantization

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A local mean-based nonparametric classifier

Cited by 177 publications

References 4 publications

Retracted: A Novel Voting Mathematical Rule Classification for Image Recognition

Retracted: A Novel Voting Mathematical Rule Classification for Image Recognition

Stimuli-Magnitude-Adaptive Sample Selection for Data-Driven Haptic Modeling

Color Image Classification Using Block Matching and Learning

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