A Survey of Recent Trends in One Class Classification

Khan, Shehroz S.; Madden, Michael G.

doi:10.1007/978-3-642-17080-5_21

Cited by 278 publications

(196 citation statements)

References 26 publications

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“…One-class classification or novelty-detection handles data where only positive examples are available and has been applied to several domains [13]. One-class SVM (OC-SVM) is perhaps the most popular method.…”

Section: Related Workmentioning

confidence: 99%

Open-Set Classification for Automated Genre Identification

Pritsos

Stamatatos

2013

Lecture Notes in Computer Science

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Abstract. Automated Genre Identification (AGI) of web pages is a problem of increasing importance since web genre (e.g. blog, news, eshops, etc.) information can enhance modern Information Retrieval (IR) systems. The state-of-the-art in this field considers AGI as a closed-set classification problem where a variety of web page representation and machine learning models have intensively studied. In this paper, we study AGI as an open-set classification problem which better formulates the real world conditions of exploiting AGI in practice. Focusing on the use of content information, different text representation methods (words and character n-grams) are tested. Moreover, two classification methods are examined, one-class SVM learners, used as a baseline, and an ensemble of classifiers based on random feature subspacing, originally proposed for author identification. It is demonstrated that very high precision can be achieved in open-set AGI while recall remains relatively high.

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Section: Related Workmentioning

confidence: 99%

Open-Set Classification for Automated Genre Identification

Pritsos

Stamatatos

2013

Lecture Notes in Computer Science

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“…The 45 non-archaeological test sites are combined with the previously known 43 archaeological sites to create a real world data set including both archaeological and nonarchaeological samples which are used to better evaluate the best method identified in our first experiment using artificially generated non-archaeological test samples. There are a number of one-class classifiers in the literature (TAX, 2001;Khan and Madden, 2010). In this paper, several sophisticated one-class classifiers that have been widely used in the literature are investigated.…”

Section: Methodsologymentioning

confidence: 99%

Application of pattern recognition in detection of buried archaeological sites based on analysing environmental variables, Khorramabad Plain, West Iran

Sharafi

Simpson

Barceló

2016

Journal of Archaeological Science: Reports

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Archaeologists continue to search for techniques that enable them to analyze archaeological data efficiently with Artificial Intelligence approaches increasingly employed to create new knowledge from archaeological data. The purpose of this paper is to investigate the application of Pattern Recognition methods in detection of buried archaeological sites of the semi-arid Khorramabad Plain located in west Iran. This environment has provided suitable conditions for human habitation for over 40,000 years. However, environmental changes in the late Pleistocene and Holocene have caused erosion and sedimentation resulting in burial of some archaeological sites making archaeological landscape reconstructions more challenging. In this paper, the environmental variables that have influenced formation of archaeological sites of the Khorramabad Plain are identified through the appliation of Arc GIS. These variables are utilized to create an accurate predictive model based on the application of One-Class classification Pattern Recognition techniques. These techniques can be built using data from one class only, when the data from other classes are difficult to obtain, and are highly suitable in this context. The experimental results of this paper confirm one-class classifiers, including Auto-encoder Neural Network, Kmeans, Principal Component Analysis Data Descriptor, Minimum Spanning Tree Data Descriptor, K-Nearest neighbor and Gaussian distribution as promising applications in creating an effective model for detecting buried archaeological sites. Among the investigated classifiers, Minimum Spanning Tree Data Descriptor achieved the best performance on the Khorramabad Plain data set.

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“…Learning from single class data, also known as OneClass Classification (OCC), involves inducing a predictive model that distinguishes examples that belong to a given class from examples that do not belong to it [20].…”

Section: One-class Classificationmentioning

confidence: 99%

Filter Feature Selection for One-Class Classification

Lorena

Carvalho

Lorena

2014

J Intell Robot Syst

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In one-class classification problems all training examples belong to a single class. The absence of counter-examples represents a challenge to traditional Machine Learning and pre-processing techniques. This is the case of various feature selection techniques for labeled data. The selection of the most relevant features from a dataset usually benefits the performance obtained by classification algorithms. Despite the relevance of this issue, few techniques have been proposed for feature selection in one-class classification problems. Moreover, most of the existent techniques are wrapper approaches, which have to rely on a specific classification algorithm for feature selection, or aggregation techniques. This paper proposes a new filter feature selection approach for one-class classification. First, five feature selection measures from different paradigms are here employed or adapted to the one-class scenario. Next, the feature rankings produced by these measures are combined using different aggregation strategies. The proposed approach was able to reduce the size of the feature sets while maintaining or even improving the predictive performance obtained by the one-class classifier.

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A Survey of Recent Trends in One Class Classification

Cited by 278 publications

References 26 publications

Open-Set Classification for Automated Genre Identification

Open-Set Classification for Automated Genre Identification

Application of pattern recognition in detection of buried archaeological sites based on analysing environmental variables, Khorramabad Plain, West Iran

Filter Feature Selection for One-Class Classification

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