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ODDS – Outlier Detection DataSets

Outlier Detection DataSets (ODDS). Outlier Detection DataSets (ODDS). Outlier Detection DataSets (ODDS). In ODDS, we openly provide access to a large collection of outlier detection datasets with ground truth (if available). Our focus is to provide datasets from different domains and present them under a single umbrella for the research community. As such, we arrange the datasets based on their types into different tables in the order as listed below. [ read more about ODDS. Entity co-mention graph for N...

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ODDS – Outlier Detection DataSets | odds.cs.stonybrook.edu Reviews

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Outlier Detection DataSets (ODDS). Outlier Detection DataSets (ODDS). Outlier Detection DataSets (ODDS). In ODDS, we openly provide access to a large collection of outlier detection datasets with ground truth (if available). Our focus is to provide datasets from different domains and present them under a single umbrella for the research community. As such, we arrange the datasets based on their types into different tables in the order as listed below. [ read more about ODDS. Entity co-mention graph for N...

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ODDS – Outlier Detection DataSets | odds.cs.stonybrook.edu Reviews

https://odds.cs.stonybrook.edu

Outlier Detection DataSets (ODDS). Outlier Detection DataSets (ODDS). Outlier Detection DataSets (ODDS). In ODDS, we openly provide access to a large collection of outlier detection datasets with ground truth (if available). Our focus is to provide datasets from different domains and present them under a single umbrella for the research community. As such, we arrange the datasets based on their types into different tables in the order as listed below. [ read more about ODDS. Entity co-mention graph for N...

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odds.cs.stonybrook.edu

1

Satimage-2 dataset – ODDS

http://odds.cs.stonybrook.edu/satimage-2-dataset

Outlier Detection DataSets (ODDS). Outlier Detection DataSets (ODDS). The original Statlog (Landsat Satellite). UCI machine learning repository. Is a multi-class classification dataset. Here, the training and test data are combined. Class 2 is down-sampled to 71 outliers, while all the other classes are combined to form an inlier class. The modified dataset is referred to as Satimage-2. Zimek , M. Gaudet, R. J. Campello, and J. Sander,. In ACM SIGKDD, 2013, pp. 428 436. C C Aggarwal and S. Sathe,.

2

Lymphography dataset – ODDS

http://odds.cs.stonybrook.edu/lympho

Outlier Detection DataSets (ODDS). Outlier Detection DataSets (ODDS). UCI machine learning repository. Is a classification dataset. It is a multi-class dataset having four classes, but two of them are quite small (2 and 4 data records). Therefore, those two small classes are merged and considered as outliers compared to other two large classes (81 and 61 data records). A Lazarevic and V. Kumar,. Feature bagging for outlier detection. In ACM SIGKDD, 2005, pp. 157 166. In ACM SIGKDD, 2013, pp. 428 436.

3

Letter Recognition dataset – ODDS

http://odds.cs.stonybrook.edu/letter-recognition-dataset

Outlier Detection DataSets (ODDS). Outlier Detection DataSets (ODDS). The original letter recognition dataset. From UCI machine learning repository. Is a multi-class classification dataset. The objective is to identify each of a large number of black-and-white rectangular pixel displays as one of the 26 capital letters in the English alphabet, where letters of the alphabet are represented in 16 dimensions. Learing Outlier Ensembles: The Best of Both Worlds – Supervised and Unsupervised.

4

Ionosphere dataset – ODDS

http://odds.cs.stonybrook.edu/ionosphere-dataset

Outlier Detection DataSets (ODDS). Outlier Detection DataSets (ODDS). The original ionosphere dataset. UCI machine learning repository. Is a binary classification dataset with dimensionality 34. There is one attribute having values all zeros, which is discarded. So the total number of dimensions are 33. The ‘bad’ class is considered as outliers class and the ‘good’ class as inliers. Liu, Fei Tony, Kai Ming Ting, and Zhi-Hua Zhou. “ Isolation forest. Mass: A New Ranking Measure for Anomaly Detection.

5

ForestCover/Covertype dataset – ODDS

http://odds.cs.stonybrook.edu/forestcovercovertype-dataset

Outlier Detection DataSets (ODDS). Outlier Detection DataSets (ODDS). UCI machine learning repository. 54 attributes (10 quantitative variables, 4 binary wilderness areas and 40 binary soil type variables). Here, outlier detection dataset is created using only 10 quantitative attributes. I. Nstances from class 2 are considered as normal points and instances from class 4 are anomalies. The anomalies ratio is 0.9%. Instances. From the other classes are omitted. Mass Estimation and Its Applications. Fast An...

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About Me

http://shebuti.com/about-me

I am a PhD student in the Department of Computer Science. At Stony Brook University. I received a B.Sc. in Computer Science and Engineering from the Department of. Computer Science and Engineering. Of Bangladesh University of Engineering and Technology (BUET). Data mining, Large-scale graph mining, Event and Anomaly detection, Machine Learning and Graph Algorithms. In Computer Science Department, Stony Brook University. Stony Brook, NY. Professor, Department of Computer Science and Engineering, BUET.

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rayana28

http://shebuti.com/author/rayana28

Author Archives : rayana28. Collective Opinion Spam Detection. Collective Opinion Spam Detection: Bridging Review Networks and Metadata Abstract Online reviews capture the testimonials of real people and help shape the decisions of other consumers. Due to the financial gains associated with positive reviews, however, opinion spam has become a widespread problem,…. Event Detection and Characterization in Dynamic Graphs. Event Detection and Characterization in Dynamic Graphs from shebuti.

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September 2014

http://shebuti.com/2014/09

Monthly Archives : September 2014. Event Detection and Characterization in Dynamic Graphs. Event Detection and Characterization in Dynamic Graphs from shebuti. Convac Theme by SketchThemes.

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June 2015

http://shebuti.com/2015/06

Monthly Archives : June 2015. Collective Opinion Spam Detection. Collective Opinion Spam Detection: Bridging Review Networks and Metadata Abstract Online reviews capture the testimonials of real people and help shape the decisions of other consumers. Due to the financial gains associated with positive reviews, however, opinion spam has become a widespread problem,…. Convac Theme by SketchThemes.

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Publications

http://shebuti.com/publications

Less is More: Building Selective Anomaly Ensemble with Application to Event Detection in Temporal Graphs. Shebuti Rayana, Leman Akoglu,. Vancouver, BC, Canada, April 2015. Collective Opinion Spam Detection: Bridging Review Networks and Metadata. Shebuti Rayana, Leman Akoglu, ACM SIGKDD. Sydney, Australia, August 10-13, 2015. Collective Opinion Spam Detection using Active Inference. Shebuti Rayana, Leman Akoglu, SIAM SDM. Miami, Florida, USA, May 5-7, 2016. Less is More: Building Selective Anomaly Ensemble.

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February 2015

http://shebuti.com/2015/02

Monthly Archives : February 2015. Less is More: Building Selective Anomaly Ensemble Abstract Ensemble techniques for classification and clustering have long proven effective, yet anomaly ensembles have been barely studied. In this work, we tap into this gap and propose a new ensemble approach for…. Convac Theme by SketchThemes.

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Projects

http://shebuti.com/category/projects

Category Archives : Projects. Sequential Ensemble Learning for Outlier Detection. CARE Sequential Ensemble Learning for Outlier Detection: A Bias-Variance Perspective We propose a sequential ensemble approach called CARE that employs a two-phase aggregation of the intermediate results. Our ensemble incorporates both the parallel and sequential building blocks to reduce bias as…. Collective Opinion Spam Detection. Event Detection and Characterization in Dynamic Graphs. Convac Theme by SketchThemes.

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odds.cs.stonybrook.edu

ODDS – Outlier Detection DataSets

Outlier Detection DataSets (ODDS). Outlier Detection DataSets (ODDS). Outlier Detection DataSets (ODDS). In ODDS, we openly provide access to a large collection of outlier detection datasets with ground truth (if available). Our focus is to provide datasets from different domains and present them under a single umbrella for the research community. As such, we arrange the datasets based on their types into different tables in the order as listed below. [ read more about ODDS. Entity co-mention graph for N...

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