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JournalISSN: 0167-8655

Pattern Recognition Letters 

About: Pattern Recognition Letters is an academic journal. The journal publishes majorly in the area(s): Cluster analysis & Image processing. It has an ISSN identifier of 0167-8655. Over the lifetime, 7574 publication(s) have been published receiving 279197 citation(s).
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Journal ArticleDOI
TL;DR: The purpose of this article is to serve as an introduction to ROC graphs and as a guide for using them in research.
Abstract: Receiver operating characteristics (ROC) graphs are useful for organizing classifiers and visualizing their performance. ROC graphs are commonly used in medical decision making, and in recent years have been used increasingly in machine learning and data mining research. Although ROC graphs are apparently simple, there are some common misconceptions and pitfalls when using them in practice. The purpose of this article is to serve as an introduction to ROC graphs and as a guide for using them in research.

14,304 citations


Journal ArticleDOI
TL;DR: Sequential search methods characterized by a dynamically changing number of features included or eliminated at each step, henceforth "floating" methods, are presented and are shown to give very good results and to be computationally more effective than the branch and bound method.
Abstract: Sequential search methods characterized by a dynamically changing number of features included or eliminated at each step, henceforth "floating" methods, are presented. They are shown to give very good results and to be computationally more effective than the branch and bound method.

2,914 citations


Journal ArticleDOI
Arun Ross1, Anil K. Jain1Institutions (1)
TL;DR: This paper addresses the problem of information fusion in biometric verification systems by combining information at the matching score level by combining three biometric modalities (face, fingerprint and hand geometry).
Abstract: User verification systems that use a single biometric indicator often have to contend with noisy sensor data, restricted degrees of freedom, non-universality of the biometric trait and unacceptable error rates Attempting to improve the performance of individual matchers in such situations may not prove to be effective because of these inherent problems Multibiometric systems seek to alleviate some of these drawbacks by providing multiple evidences of the same identity These systems help achieve an increase in performance that may not be possible using a single biometric indicator Further, multibiometric systems provide anti-spoofing measures by making it difficult for an intruder to spoof multiple biometric traits simultaneously However, an effective fusion scheme is necessary to combine the information presented by multiple domain experts This paper addresses the problem of information fusion in biometric verification systems by combining information at the matching score level Experimental results on combining three biometric modalities (face, fingerprint and hand geometry) are presented

1,545 citations


Journal ArticleDOI
David M. J. Tax1, Robert P. W. Duin1Institutions (1)
TL;DR: This paper shows the use of a data domain description method, inspired by the support vector machine by Vapnik, called the support vectors domain description (SVDD), which can be used for novelty or outlier detection and is compared with other outlier Detection methods on real data.
Abstract: This paper shows the use of a data domain description method, inspired by the support vector machine by Vapnik, called the support vector domain description (SVDD). This data description can be used for novelty or outlier detection. A spherically shaped decision boundary around a set of objects is constructed by a set of support vectors describing the sphere boundary. It has the possibility of transforming the data to new feature spaces without much extra computational cost. By using the transformed data, this SVDD can obtain more flexible and more accurate data descriptions. The error of the first kind, the fraction of the training objects which will be rejected, can be estimated immediately from the description without the use of an independent test set, which makes this method data eAcient. The support vector domain description is compared with other outlier detection methods on real data. ” 1999 Elsevier Science B.V. All rights reserved.

1,482 citations


Journal ArticleDOI
TL;DR: This paper proposes, focusing on random forests, the increasingly used statistical method for classification and regression problems introduced by Leo Breiman in 2001, to investigate two classical issues of variable selection, and proposes a strategy involving a ranking of explanatory variables using the random forests score of importance and a stepwise ascending variable introduction strategy.
Abstract: This paper proposes, focusing on random forests, the increasingly used statistical method for classification and regression problems introduced by Leo Breiman in 2001, to investigate two classical issues of variable selection. The first one is to find important variables for interpretation and the second one is more restrictive and try to design a good parsimonious prediction model. The main contribution is twofold: to provide some experimental insights about the behavior of the variable importance index based on random forests and to propose a strategy involving a ranking of explanatory variables using the random forests score of importance and a stepwise ascending variable introduction strategy.

1,397 citations


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Performance
Metrics
No. of papers from the Journal in previous years
YearPapers
2021369
2020506
2019347
2018222
2017353
2016297