909 research outputs found
A Local Density-Based Approach for Local Outlier Detection
This paper presents a simple but effective density-based outlier detection
approach with the local kernel density estimation (KDE). A Relative
Density-based Outlier Score (RDOS) is introduced to measure the local
outlierness of objects, in which the density distribution at the location of an
object is estimated with a local KDE method based on extended nearest neighbors
of the object. Instead of using only nearest neighbors, we further consider
reverse nearest neighbors and shared nearest neighbors of an object for density
distribution estimation. Some theoretical properties of the proposed RDOS
including its expected value and false alarm probability are derived. A
comprehensive experimental study on both synthetic and real-life data sets
demonstrates that our approach is more effective than state-of-the-art outlier
detection methods.Comment: 22 pages, 14 figures, submitted to Pattern Recognition Letter
Machine Learning in Wireless Sensor Networks: Algorithms, Strategies, and Applications
Wireless sensor networks monitor dynamic environments that change rapidly
over time. This dynamic behavior is either caused by external factors or
initiated by the system designers themselves. To adapt to such conditions,
sensor networks often adopt machine learning techniques to eliminate the need
for unnecessary redesign. Machine learning also inspires many practical
solutions that maximize resource utilization and prolong the lifespan of the
network. In this paper, we present an extensive literature review over the
period 2002-2013 of machine learning methods that were used to address common
issues in wireless sensor networks (WSNs). The advantages and disadvantages of
each proposed algorithm are evaluated against the corresponding problem. We
also provide a comparative guide to aid WSN designers in developing suitable
machine learning solutions for their specific application challenges.Comment: Accepted for publication in IEEE Communications Surveys and Tutorial
Improving Influenced Outlierness(INFLO) Outlier Detection Method
Anomaly detection refers to the process of finding outlying records from a given dataset.This process is a subject of increasing interest among analysts. Anomaly detection is a subject of interest in various knowledge domains. As the size of data is doubling every three years there is a need to detect anomalies in large datasets as fast as possible. Another need is the availability of unsupervised methods for the same. This thesis aims at implement and comparing few of the state of art unsupervised outlier detection methods and propose a way to better them. This thesis goes in depth about the implementation and analysis of outlier detection algorithms such as Local Outlier Factor(LOF),Connectivity-Based Outlier Factor(COF),Local Distance-Based Outlier Factor and Influenced Outlierness. The concepts of these methods are then combined to propose a new method which better the previous mentioned ones in terms of speed and accuracy
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