Boosting Positive and Unlabeled Learning for Anomaly Detection With Multi-Features

2019 ◽  
Vol 21 (5) ◽  
pp. 1332-1344 ◽  
Author(s):  
Jiaqi Zhang ◽  
Zhenzhen Wang ◽  
Jingjing Meng ◽  
Yap-Peng Tan ◽  
Junsong Yuan
Keyword(s):  
Anomaly Detection ◽  
Positive And Unlabeled Learning

scholarly journals Class Prior Estimation in Active Positive and Unlabeled Learning

2020 ◽  
Author(s):  
Lorenzo Perini ◽  
Vincent Vercruyssen ◽  
Jesse Davis
Keyword(s):  
Active Learning ◽  
Anomaly Detection ◽  
Learning Strategy ◽  
Important Task ◽  
Positive And Unlabeled Learning ◽  
Active Learning Strategy

Estimating the proportion of positive examples (i.e., the class prior) from positive and unlabeled (PU) data is an important task that facilitates learning a classifier from such data. In this paper, we explore how to tackle this problem when the observed labels were acquired via active learning. This introduces the challenge that the observed labels were not selected completely at random, which is the primary assumption underpinning existing approaches to estimating the class prior from PU data. We analyze this new setting and design an algorithm that is able to estimate the class prior for a given active learning strategy. Empirically, we show that our approach accurately recovers the true class prior on a benchmark of anomaly detection datasets and that it does so more accurately than existing methods.

Anomaly Detection in Sensor Data

2018 ◽  
Vol 18 (1) ◽  
pp. 20-32 ◽  
Author(s):  
Jong-Min Kim ◽  
Jaiwook Baik
Keyword(s):  
Anomaly Detection ◽  
Sensor Data

An Anomaly Detection Method with Exemplar Subsequence for Time Series Data

2016 ◽  
Vol 136 (3) ◽  
pp. 363-372
Author(s):  
Takaaki Nakamura ◽  
Makoto Imamura ◽  
Masashi Tatedoko ◽  
Norio Hirai
Keyword(s):  
Time Series ◽  
Anomaly Detection ◽  
Time Series Data ◽  
Detection Method ◽  
Series Data

Proposal of the Anomaly Detection Method of Live-alone by LOF using Multiple Sensors

2015 ◽  
Vol 135 (12) ◽  
pp. 749-755
Author(s):  
Taiyo Matsumura ◽  
Ippei Kamihira ◽  
Katsuma Ito ◽  
Takashi Ono
Keyword(s):  
Anomaly Detection ◽  
Detection Method ◽  
Multiple Sensors
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