scholarly journals One-Class Classifiers: A Review and Analysis of Suitability in the Context of Mobile-Masquerader Detection

2007 ◽  
Vol Volume 6, april 2007, joint... ◽  
Author(s):  
Oleksiy Mazhelis
Keyword(s):  
Classification Method ◽  
Classification Methods ◽  
User Characteristics ◽  
Training Set ◽  
Legitimate User ◽  
International Audience ◽  
One Class Classification

International audience One-class classifiers employing for training only the data from one class are justified when the data from other classes is difficult to obtain. In particular, their use is justified in mobile-masquerader detection, where user characteristics are classified as belonging to the legitimate user class or to the impostor class, and where collecting the data originated from impostors is problematic. This paper systematically reviews various one-class classification methods, and analyses their suitability in the context of mobile-masquerader detection. For each classification method, its sensitivity to the errors in the training set, computational requirements, and other characteristics are considered. After that, for each category of features used in masquerader detection, suitable classifiers are identified.

scholarly journals A Novel Unsupervised Classification Method for Sandy Land Using Fully Polarimetric SAR Data

2021 ◽  
Vol 13 (3) ◽  
pp. 355
Author(s):  
Weixian Tan ◽  
Borong Sun ◽  
Chenyu Xiao ◽  
Pingping Huang ◽  
Wei Xu ◽  
...  
Keyword(s):  
Spectral Clustering ◽  
Large Scale ◽  
Clustering Algorithm ◽  
Feature Vector ◽  
Unsupervised Classification ◽  
Classification Method ◽  
Sandy Land ◽  
Classification Methods ◽  
The Many ◽  
Representative Points

Classification based on polarimetric synthetic aperture radar (PolSAR) images is an emerging technology, and recent years have seen the introduction of various classification methods that have been proven to be effective to identify typical features of many terrain types. Among the many regions of the study, the Hunshandake Sandy Land in Inner Mongolia, China stands out for its vast area of sandy land, variety of ground objects, and intricate structure, with more irregular characteristics than conventional land cover. Accounting for the particular surface features of the Hunshandake Sandy Land, an unsupervised classification method based on new decomposition and large-scale spectral clustering with superpixels (ND-LSC) is proposed in this study. Firstly, the polarization scattering parameters are extracted through a new decomposition, rather than other decomposition approaches, which gives rise to more accurate feature vector estimate. Secondly, a large-scale spectral clustering is applied as appropriate to meet the massive land and complex terrain. More specifically, this involves a beginning sub-step of superpixels generation via the Adaptive Simple Linear Iterative Clustering (ASLIC) algorithm when the feature vector combined with the spatial coordinate information are employed as input, and subsequently a sub-step of representative points selection as well as bipartite graph formation, followed by the spectral clustering algorithm to complete the classification task. Finally, testing and analysis are conducted on the RADARSAT-2 fully PolSAR dataset acquired over the Hunshandake Sandy Land in 2016. Both qualitative and quantitative experiments compared with several classification methods are conducted to show that proposed method can significantly improve performance on classification.

Imbalanced Data Classification Based on Clustering

2013 ◽  
Vol 443 ◽  
pp. 741-745
Author(s):  
Hu Li ◽  
Peng Zou ◽  
Wei Hong Han ◽  
Rong Ze Xia
Keyword(s):  
Real World ◽  
Imbalanced Data ◽  
Data Classification ◽  
Comprehensive Analysis ◽  
Classification Method ◽  
Classification Methods ◽  
Real World Data ◽  
Minority Class ◽  
Imbalanced Data Classification ◽  
Traditional Classification

Many real world data is imbalanced, i.e. one category contains significantly more samples than other categories. Traditional classification methods take different categories equally and are often ineffective. Based on the comprehensive analysis of existing researches, we propose a new imbalanced data classification method based on clustering. The method clusters both majority class and minority class at first. Then, clustered minority class will be over-sampled by SMOTE while clustered majority class be under-sampled randomly. Through clustering, the proposed method can avoid the loss of useful information while resampling. Experiments on several UCI datasets show that the proposed method can effectively improve the classification results on imbalanced data.

scholarly journals Improvement in Purity of Healthy Tomato Seeds Using an Image-Based One-Class Classification Method

Sensors ◽  
2020 ◽  
Vol 20 (9) ◽  
pp. 2690 ◽  
Author(s):  
Jannat Yasmin ◽  
Santosh Lohumi ◽  
Mohammed Raju Ahmed ◽  
Lalit Mohan Kandpal ◽  
Mohammad Akbar Faqeerzada ◽  
...  
Keyword(s):  
Machine Vision ◽  
Vision System ◽  
Light Emitting Diode ◽  
Germination Rate ◽  
Classification Method ◽  
Tomato Seeds ◽  
Significant Difference ◽  
Foreign Materials ◽  
The One ◽  
One Class Classification

The feasibility of a color machine vision technique with the one-class classification method was investigated for the quality assessment of tomato seeds. The health of seeds is an important quality factor that affects their germination rate, which may be affected by seed contamination. Hence, segregation of healthy seeds from diseased and infected seeds, along with foreign materials and broken seeds, is important to improve the final yield. In this study, a custom-built machine vision system containing a color camera with a white light emitting diode (LED) light source was adopted for image acquisition. The one-class classification method was used to identify healthy seeds after extracting the features of the samples. A significant difference was observed between the features of healthy and infected seeds, and foreign materials, implying a certain threshold. The results indicated that tomato seeds can be classified with an accuracy exceeding 97%. The infected tomato seeds indicated a lower germination rate (<10%) compared to healthy seeds, as confirmed by the organic growing media germination test. Thus, identification through image analysis and rapid measurement were observed as useful in discriminating between the quality of tomato seeds in real time.

A New Network Traffic Classification Method Based on Optimized Hadamard Matrix and ECOC-SVM

2014 ◽  
Vol 989-994 ◽  
pp. 1895-1900
Author(s):  
Hong Zhi Wang ◽  
Li Hui Yan
Keyword(s):  
Network Traffic ◽  
Classification Accuracy ◽  
Hadamard Matrix ◽  
Classification Method ◽  
Traffic Classification ◽  
Classification Methods ◽  
Computational Burden ◽  
Network Traffic Classification ◽  
Encrypted Traffic

The traditional network traffic classification methods have many shortcomings, the classification accuracy is not high, the encrypted traffic cannot be analyzed, and the computational burden is usually large. To overcome above problems, this paper presents a new network traffic classification method based on optimized Hadamard matrix and ECOC. Through restructuring the Hadamard matrix and erasing the interference rows and columns, the ECOC table is optimized while eliminating SVM sample imbalance, and the error correcting ability for classification is reserved. The experiments results show that the proposed method outperform in network traffic classification and improve the classification accuracy.

On the Evaluation of Outlier Detection and One-Class Classification Methods

2016 ◽  
Author(s):  
Lorne Swersky ◽  
Henrique O. Marques ◽  
Joerg Sander ◽  
Ricardo J.G.B. Campello ◽  
Arthur Zimek
Keyword(s):  
Outlier Detection ◽  
Classification Methods ◽  
One Class Classification

scholarly journals Lead Detection in Polar Oceans—A Comparison of Different Classification Methods for Cryosat-2 SAR Data

2018 ◽  
Vol 10 (8) ◽  
pp. 1190 ◽  
Author(s):  
Denise Dettmering ◽  
Alan Wynne ◽  
Felix Müller ◽  
Marcello Passaro ◽  
Florian Seitz
Keyword(s):  
Unsupervised Classification ◽  
Detection Algorithm ◽  
Classification Method ◽  
Correct Identification ◽  
Polar Regions ◽  
Classification Methods ◽  
Lead Detection ◽  
Radar Echoes ◽  
Polar Oceans ◽  
Sar Data

In polar regions, sea-ice hinders the precise observation of Sea Surface Heights (SSH) by satellite altimetry. In order to derive reliable heights for the openings within the ice, two steps have to be fulfilled: (1) the correct identification of water (e.g., in leads or polynias), a process known as lead classification; and (2) dedicated retracking algorithms to extract the ranges from the radar echoes. This study focuses on the first point and aims at identifying the best available lead classification method for Cryosat-2 SAR data. Four different altimeter lead classification methods are compared and assessed with respect to very high resolution airborne imagery. These methods are the maximum power classifier; multi-parameter classification method primarily based on pulse peakiness; multi-observation analysis of stack peakiness; and an unsupervised classification method. The unsupervised classification method with 25 clusters consistently performs best with an overall accuracy of 97%. Furthermore, this method does not require any knowledge of specific ice characteristics within the study area and is therefore the recommended lead detection algorithm for Cryosat-2 SAR in polar oceans.

Coupling Sentiment Dictionary and SVM Classification for Text Orientation Analysis

2014 ◽  
Vol 989-994 ◽  
pp. 2444-2449
Author(s):  
Ming Ze Gao ◽  
Fang Fang Li ◽  
Zhe Yuan Ding ◽  
Wei Dong Xiao
Keyword(s):  
Classification Accuracy ◽  
Opinion Mining ◽  
Svm Classifier ◽  
Classification Methods ◽  
Initial Training ◽  
Training Set ◽  
Orientation Analysis ◽  
Second Stage ◽  
Sentiment Dictionary ◽  
Two Stages

Sentiment classification finds various applications in opinion mining, which can help users determine sentiment tendency of texts and information. In this paper, we consider the problem of text orientation analysis. In particular, we propose a two-stage approach by coupling sentiment dictionary and classification methods. In the first stage, we build sentiment dictionary and rules to obtain the texts whose emotional scores are ranked in the top 1/4 and the bottom 1/4. These texts are marked classified for supervising the second stage. In the second stage, we employ the SVM classifier to process the remaining texts. Finally, we combine the two stages to get the orientation analysis results for all the texts. Experimental results demonstrate that, in contrast to using sentiment dictionary and classification method separately, our proposed method achieves higher classification accuracy when an initial training set by manual tagging is unavailable.

scholarly journals Nouvelle approche d’accélération du codage fractal d’images

2009 ◽  
Vol Volume 11, 2009 - Special... ◽  
Author(s):  
Sofia Douda ◽  
Abdelhakim El Imrani ◽  
Mohammed Limouri
Keyword(s):  
Computational Complexity ◽  
Image Quality ◽  
Compression Ratio ◽  
Classification Methods ◽  
Fractal Image Compression ◽  
Fractal Image ◽  
Fast Decoding ◽  
Reduction Methods ◽  
Speed Up ◽  
International Audience

International audience The Fractal image compression has the advantage of presenting fast decoding and independent resolution but it suffers of slow encoding phase. In the present study, we propose to reduce the computational complexity by using two domain pools instead of one domain pool and encoding an image in two steps (AP2D approach). AP2D could be applied to classification methods or domain pool reduction methods leading to more reduction in encoding phase. Indeed, experimental results showed that AP2D speed up the encoding time. The time reduction obtained reached a percentage of more than 65% when AP2D was applied to Fisher classification and more than 72% when AP2D was applied to exhaustive search. The image quality was not altered by this approach while the compression ratio was slightly enhanced. La compression fractale d’images permet un décodage rapide et une indépendance de la résolution mais souffre d’une lenteur dans le codage. Le présent travail présente une approche visant à réduire le temps de calcul en utilisant deux dictionnaires et une approximation de l’image en deux étapes (AP2D). L’approche AP2D peut être appliquée aux méthodes de classification ou aux méthodes de réduction du cardinal du dictionnaire et ainsi réduire davantage le temps de codage. Les résultats expérimentaux ont montré que AP2D appliquée à une recherche exhaustive a atteint un gain de temps de plus de 72%. De même AP2D appliquée à la classification de Fisher a permis une réduction de temps de codage de plus de 65%. La qualité de l’image n’a pas été altérée par cette approche et le taux de compression a légèrement augmenté.

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