Fast Outlier Detection Using
Rough Sets Theory
Author(s)
F. Shaari, A. A. Bakar & A. R. Hamdan
Abstract
In many Knowledge Discovery applications, finding outliers is more interesting
than finding inliers in a dataset. The perception of outliers is rare cases in dataset
in which is being described as abnormal data in the information table. Outliers
detections are applied in many important applications like fraud detection
systems to uncover the suspicious objects which may have important knowledge
hidden in the system. A new outlier detection technique based on Rough Sets
Theory (RST) is hereby proposed. RSetOF is a new measure for the outlier
factor based on RST. By employing this factor, a new formulation for detecting
outlier is established. The outlyingness of outliers objects in a dataset using this
measurement is identified. To detect outliers, two measurements which are the
top n ratio and the coverage ratio are presented. Finding top n outliers from all
objects allow searching of outliers from top ranked records based on the least
outlier factor value. The capability in detecting outliers at top n number of
outliers will indicate how fast the detection is. The efficiency of this technique
by obtaining the coverage ratio value is then tested. The maximum percentage of
coverage obtained shows the maximum number of outliers detected belonging to
rare cases. A comparison is hence carried out to examine the performance of the
RSetAlg with a selective outlier detection method, the Frequent Pattern method
referred to as FindFPOF. Ten benchmark datasets for assessing the outlier
detection technique are used for this purpose. The experimental result shows that
the proposed technique is competitive and proven to be better in speed of
detection than the other technique. The fast and efficient detection of outliers has
proven its potential as a new outliers detection technique based on RST.
Keywords: outlier detection, rare, deviate, exception, deviation, anomaly,
infrequent, small, imbalance.
Keywords
outlier detection, rare, deviate, exception, deviation, anomaly,infrequent, small, imbalance.
Related Book
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