7,613 research outputs found
Automatic Hyperparameter Tuning Method for Local Outlier Factor, with Applications to Anomaly Detection
In recent years, there have been many practical applications of anomaly
detection such as in predictive maintenance, detection of credit fraud, network
intrusion, and system failure. The goal of anomaly detection is to identify in
the test data anomalous behaviors that are either rare or unseen in the
training data. This is a common goal in predictive maintenance, which aims to
forecast the imminent faults of an appliance given abundant samples of normal
behaviors. Local outlier factor (LOF) is one of the state-of-the-art models
used for anomaly detection, but the predictive performance of LOF depends
greatly on the selection of hyperparameters. In this paper, we propose a novel,
heuristic methodology to tune the hyperparameters in LOF. A tuned LOF model
that uses the proposed method shows good predictive performance in both
simulations and real data sets.Comment: 15 pages, 5 figure
Anomaly Detection Based on Aggregation of Indicators
Automatic anomaly detection is a major issue in various areas. Beyond mere
detection, the identification of the origin of the problem that produced the
anomaly is also essential. This paper introduces a general methodology that can
assist human operators who aim at classifying monitoring signals. The main idea
is to leverage expert knowledge by generating a very large number of
indicators. A feature selection method is used to keep only the most
discriminant indicators which are used as inputs of a Naive Bayes classifier.
The parameters of the classifier have been optimized indirectly by the
selection process. Simulated data designed to reproduce some of the anomaly
types observed in real world engines.Comment: 23rd annual Belgian-Dutch Conference on Machine Learning (Benelearn
2014), Bruxelles : Belgium (2014
Anomaly Detection Based on Indicators Aggregation
Automatic anomaly detection is a major issue in various areas. Beyond mere
detection, the identification of the source of the problem that produced the
anomaly is also essential. This is particularly the case in aircraft engine
health monitoring where detecting early signs of failure (anomalies) and
helping the engine owner to implement efficiently the adapted maintenance
operations (fixing the source of the anomaly) are of crucial importance to
reduce the costs attached to unscheduled maintenance. This paper introduces a
general methodology that aims at classifying monitoring signals into normal
ones and several classes of abnormal ones. The main idea is to leverage expert
knowledge by generating a very large number of binary indicators. Each
indicator corresponds to a fully parametrized anomaly detector built from
parametric anomaly scores designed by experts. A feature selection method is
used to keep only the most discriminant indicators which are used at inputs of
a Naive Bayes classifier. This give an interpretable classifier based on
interpretable anomaly detectors whose parameters have been optimized indirectly
by the selection process. The proposed methodology is evaluated on simulated
data designed to reproduce some of the anomaly types observed in real world
engines.Comment: International Joint Conference on Neural Networks (IJCNN 2014),
Beijing : China (2014). arXiv admin note: substantial text overlap with
arXiv:1407.088
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