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Fair Outlier Detection [chapter]

P. Deepak, Savitha Sam Abraham
2020 Lecture Notes in Computer Science  
Being a novel task, we develop an evaluation framework for fair outlier detection, and use that to benchmark FairLOF on quality and fairness of results.  ...  In this paper, we consider, for the first time to our best knowledge, the task of fair outlier detection.  ...  Outlier Detection and Fairness: The task of outlier detection targets to identify deviant observations from a dataset, and is usually modelled as an unsuper-vised task; [8] provides a review of outlier  ... 
doi:10.1007/978-3-030-62008-0_31 fatcat:kt5v7o2rgnaltmneheiiv32bfy

Fair Outlier Detection [article]

Deepak P, Savitha Sam Abraham
2020 arXiv   pre-print
Being a novel task, we develop an evaluation framework for fair outlier detection, and use that to benchmark FairLOF on quality and fairness of results.  ...  In this task, we consider, for the first time to our best knowledge, the task of fair outlier detection.  ...  Outlier Detection and Fairness: The task of outlier detection targets to identify deviant observations from a dataset, and is usually modelled as an unsuper-vised task; [8] provides a review of outlier  ... 
arXiv:2005.09900v2 fatcat:a3u6flg4prgudm2dcteyjdwdy4

FP-outlier: Frequent pattern based outlier detection

Zengyou He, Xiaofei Xu, Zhexue Huang, Shengchun Deng
2005 Computer Science and Information Systems  
We define a measure called FPOF (Frequen Pa ern Outlier Factor) to detect the outlier transactions and propose the FindFPOF algorithm to discover outliers.  ...  Detection of such outliers is important for many applications and has recently attracted much attention in the data mining research community.  ...  ) and the IBM SUR Research Fund supported this research.  ... 
doi:10.2298/csis0501103h fatcat:yvnwv3zelfelbmixxvlpmv5a7y

Spatial outlier detection

Xutong Liu, Chang-Tien Lu, Feng Chen
2010 Proceedings of the 18th SIGSPATIAL International Conference on Advances in Geographic Information Systems - GIS '10  
Spatial outlier detection has been an important part of spatial data mining and attracted attention in the past decades. Numerous SOD (Spatial Outlier Detection) approaches have been proposed.  ...  However, in these techniques, there exist the problems of masking and swamping.  ...  RW based techniques have been widely used for varieties of data mining tasks, including clustering [9, 11] and outlier detection [12, 18, 26] .  ... 
doi:10.1145/1869790.1869841 dblp:conf/gis/LiuLC10 fatcat:rm3va7zodvdybebbqori36haoe

Outlier detection special issue

Sanjay Chawla, David Hand, Vasant Dhar
2010 Data mining and knowledge discovery  
Or more simply, the focus in Data Mining is on non-parametric (and semi-parametric) outlier detection techniques.  ...  The presence of outliers often distorts the understanding of the norm and has given rise to a set of techniques, often called robust statistics, which discount the effect of outliers.  ...  for outlier detection.  ... 
doi:10.1007/s10618-009-0163-0 fatcat:oaekiiwzzndztmcirca6ykggrq

Outlier Detection, Spatial [chapter]

Yufeng Kou, Chang-Tien Lu
2017 Encyclopedia of GIS  
constraints, linear inequality constraints, and polynomial inequality constraints.  ...  therefore loses out on relational theory advantages, such as uniqueness of the primary key, referential integrity, and the query evaluation improvement by using indexation, clustering, and hashing, all  ...  Acknowledgements This work has been partially funded by the Ministry of Science and Technology of Spain (TIN2015-63502-C3-2-R) and the European Regional Development Fund (ERDF/FEDER).  ... 
doi:10.1007/978-3-319-17885-1_945 fatcat:p5vw3xpawfeh3jjib5qpqyjlo4

Fluctuation-based Outlier Detection [article]

Xusheng Du, Enguang Zuo, Zhenzhen He, Jiong Yu
2022 arXiv   pre-print
Outlier detection is an important topic in machine learning and has been used in a wide range of applications. Outliers are objects that are few in number and deviate from the majority of objects.  ...  This article proposes a method called fluctuation-based outlier detection (FBOD) that achieves a low linear time complexity and detects outliers purely based on the concept of fluctuation without employing  ...  Acknowledgments This research was supported by the National Natural Science Foundation of China under grants 61862060, 61462079, 61562086, and 61562078.  ... 
arXiv:2204.10007v1 fatcat:gvyqognxqjaq7mucor6fjk4xai

An Influential Algorithm for Outlier Detection

Nidhi Sharma
2015 INTERNATIONAL JOURNAL OF EMERGING TRENDS IN SCIENCE AND TECHNOLOGY  
In the paper we had detailed studied about the different-different ways for detecting outliers.  ...  Which is clustering, in the clustering there is the major issue to eradicate outliers from the data. Here we do work for detect outliers from the data sets.  ...  In this duration there is outlier detection is major issue in clustering: those data or object whose different in behavior or nature are declared as a outlier and outlier detection technique is a process  ... 
doi:10.18535/ijetst/v2i10.05 fatcat:btp27anbjvee7ewnd746v3glhm

Cross-Outlier Detection [chapter]

Spiros Papadimitriou, Christos Faloutsos
2003 Lecture Notes in Computer Science  
The problem of outlier detection has been studied in the context of several domains and has received attention from the database research community.  ...  Many of the existing outlier detection approaches cannot be extended to this case.  ...  Of course, this far from exhausts all possible pruning techniques.  ... 
doi:10.1007/978-3-540-45072-6_12 fatcat:m2hoisnn4fbcxhbzipmmjbupri

Concentration Free Outlier Detection [chapter]

Fabrizio Angiulli
2017 Lecture Notes in Computer Science  
We present a very efficient technique, named fast-CFOF, for detecting outliers in very large high-dimensional datasets.  ...  Experimental results witness for scalability and effectiveness of the technique and highlight that CFOF exhibits state of the art detection performances.  ...  Introduction Outlier detection is a prominent data mining task, whose goal is to single out anomalous observations, also called outliers [2] .  ... 
doi:10.1007/978-3-319-71249-9_1 fatcat:fo2rkkq5kzcg5ptllq6xyf6fwy

Knowledge Reused Outlier Detection

Weiren Yu, Zhengming Ding, Chunming Hu, Hongfu Liu
2019 IEEE Access  
INDEX TERMS Outlier detection, transfer learning, K-means, joint clustering and outlier detection, knowledge transfer, knowledge reuse.  ...  We have used four real-world data sets and eight outlier detection methods of different kinds for extensive experiments and comparison.  ...  Based on this, the objective function is provided for the knowledge reused outlier detection task.  ... 
doi:10.1109/access.2019.2906644 fatcat:ie2mt52vizhrrl6etnbtgh6tgm

RANSAC FOR OUTLIER DETECTION

Birutė Ruzgienė, Wolfgang Förstner
2012 Geodesy and Cartography  
One of the key tasks is to detect outliers in given data, eg for curve fitting or image matching.  ...  The problem is hard as the number of outliers is usually large, possibly larger than 50%, thus powerful estimation techniques are needed.  ...  RANSAC is a technique which is best suited for estimation problems with a small number of parameters and a large percentage of outliers.  ... 
doi:10.3846/13921541.2005.9636670 fatcat:3nrb7xmew5hdnk2em3iugggb4q

Deep Anomaly Detection with Outlier Exposure [article]

Dan Hendrycks and Mantas Mazeika and Thomas Dietterich
2019 arXiv   pre-print
In extensive experiments on natural language processing and small- and large-scale vision tasks, we find that Outlier Exposure significantly improves detection performance.  ...  This enables anomaly detectors to generalize and detect unseen anomalies.  ...  MULTICLASS CLASSIFICATION In what follows, we use Outlier Exposure to enhance the performance of existing OOD detection techniques with multiclass classification as the original task.  ... 
arXiv:1812.04606v3 fatcat:ysvcjchvvrchdgps7f63ve5yoi

Maximum Margin/Volume Outlier Detection

Shukai Li, Ivor W. Tsang
2011 2011 IEEE 23rd International Conference on Tools with Artificial Intelligence  
To this end, we alter the recently developed label generating technique, which efficiently solves a convex relaxation of the MIP problem of outlier detection.  ...  Besides these, to further enhance the efficacy of our outlier detector, we also explore the use of maximum volume criterion to measure the quality of separation between outliers and normal patterns.  ...  AP@k is also commonly used in retrieval tasks [40] and in outlier detection [41] . B.  ... 
doi:10.1109/ictai.2011.63 dblp:conf/ictai/LiT11 fatcat:7pt5pjglzrbb3bq346mie6p4x4

Local Outlier Detection with Interpretation [chapter]

Xuan Hong Dang, Barbora Micenková, Ira Assent, Raymond T. Ng
2013 Lecture Notes in Computer Science  
We demonstrate the appealing performance of LODI via a number of synthetic and real world datasets and compare its outlier detection rates against state-of-the-art algorithms.  ...  Outlier detection aims at searching for a small set of objects that are inconsistent or considerably deviating from other objects in a dataset.  ...  Part of this work has been supported by the Danish Council for Independent Research -Technology and Production Sciences (FTP), grant 10-081972.  ... 
doi:10.1007/978-3-642-40994-3_20 fatcat:wya2mjnxibgkxatcyaozsbmney
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