Fuzzy support vector machine based on within-class scatter for classification problems with outliers or noises

An, Wenjuan; Liang, Mangui

doi:10.1016/j.neucom.2012.11.023

Cited by 97 publications

(34 citation statements)

References 20 publications

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“…Bootkrajang and Kabán (2012) report results for the classification of entire images with the purpose of image revival, but not for a classification on a pixel level. Non-probabilistic methods focus on making nonprobabilistic classifiers such as support vector machines (SVM) tolerant to label noise (An & Liang, 2013), but typically do not estimate the parameters of a noise model.…”

Section: Related Workmentioning

confidence: 99%

Using Label Noise Robust Logistic Regression for Automated Updating of Topographic Geospatial Databases

Maas¹,

Rottensteiner²,

Heipke³

2016

ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci.

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ABSTRACT:Supervised classification of remotely sensed images is a classical method to update topographic geospatial databases. The task requires training data in the form of image data with known class labels, whose generation is time-consuming. To avoid this problem one can use the labels from the outdated database for training. As some of these labels may be wrong due to changes in land cover, one has to use training techniques that can cope with wrong class labels in the training data. In this paper we adapt a label noise tolerant training technique to the problem of database updating. No labelled data other than the existing database are necessary. The resulting label image and transition matrix between the labels can help to update the database and to detect changes between the two time epochs. Our experiments are based on different test areas, using real images with simulated existing databases. Our results show that this method can indeed detect changes that would remain undetected if label noise were not considered in training.

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Section: Related Workmentioning

confidence: 99%

Using Label Noise Robust Logistic Regression for Automated Updating of Topographic Geospatial Databases

Maas¹,

Rottensteiner²,

Heipke³

2016

ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci.

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“…In fact, this fuzzy membership value determines how important it is to classify a data sample correctly. Different fuzzy membership functions have different effects on the FSVM or FSTM algorithm, At present, a prevalent methods for most people is to define a fuzzy membership basing on the distance between each point and its class center [9][10]. However, both of them ignore the affinity among sample points, which results in failing to differentiate valid samples from outliers or noise.…”

Section: Fuzzy Membershipmentioning

confidence: 99%

A Fuzzy Support Tensor Machines based on Support Vector Data Description

zhang¹

2016

Proceedings of the Fourth International Conference on Information Science and Cloud Computing — PoS(ISCC2015)

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Most of the traditional machine learning algorithms are based on the vector, but in tensor space, Tensor learning is helpful to overcome the over-fitting problem than vector learning. In the meanwhile, these algorithms based on tensor require a smaller set of decision variables as compared to those approaches based on vector. Support tensor machine (STM) is a prevalent machine learning approach, and STM is widely applied to practical many problems. However, STM is sensitive to noise or outliers in the sample dataset. Fuzzy support tensor machine (FSTM) can partly overcome this shortcoming by assigning different fuzzy membership to different training samples. How to choose the appropriate fuzzy memberships is one of the important things for machine learning. At present, most of people define a fuzzy membership by introducing the distance between each point and its class centre. In this paper, We present a novel methods to compute fuzzy memberships based on support vector data description (SVDD), our experimental results on ORL database and Yale database show that the improved method can reduce the effect of outliers and improve the rate of classification accuracy. 2015 18-19, December, ISCC

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“…In this reference [14] (2013), as SVM is sensitive to outliers or noises in the dataset, Fuzzy SVM (FSVM) used. Like SVM, despite everything it goes for discovering an optimal hyperplane that can isolate two classes with the maximal margin.…”

Section: Fuzzy Theory Based Support Vector Machine Classifier (2008)mentioning

confidence: 99%

Fuzzy Support Vector Machines for Face Recognition: A Review

Prakash¹,

Singh²

2015

IJCA

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Support vector machine (SVMs) is a classical classification tool in face recognition. In ordinary SVM, every input points are considered to have the same commitment to the training model. On the other hand, this is not generally valid due to some challenges in face recognition. Since there may be a few points undermined by commotion so they are less significant and the machine ought to better to toss them which are undecidable. This paper review some methodology to handle this sort of information giving so as to utilize fuzzy methodology them a weight which demonstrate the diverse commitment of every point to the model. The weights are resolved as for their membership function. Such approach is typically called as Fuzzy SVM (FSVM).

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Fuzzy support vector machine based on within-class scatter for classification problems with outliers or noises

Cited by 97 publications

References 20 publications

Using Label Noise Robust Logistic Regression for Automated Updating of Topographic Geospatial Databases

Using Label Noise Robust Logistic Regression for Automated Updating of Topographic Geospatial Databases

A Fuzzy Support Tensor Machines based on Support Vector Data Description

Fuzzy Support Vector Machines for Face Recognition: A Review

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