NDLI: Large symmetric margin instance selection algorithm

Content Provider	Springer Nature Link
Author	Hamidzadeh, Javad Monsefi, Reza Sadoghi Yazdi, Hadi
Copyright Year	2014
Abstract	In instance-based classifiers, there is a need for storing a large number of samples as a training set. In this paper, we propose a large symmetric margin instance selection algorithm, namely LAMIS. LAMIS removes non-border (interior) instances and keeps border ones. This paper presents an instance selection process through formulating it as a constrained binary optimization problem and solves it by employment filled function algorithm. Instance-based learning algorithms are often confronted with the problem of deciding which instances must be stored for use during an actual test. Storing too many instances can result in large memory requirements and slow execution. In LAMIS, the core of instance selection process is based on keeping the hyperplane that separates a two-class data, to provide large margin separation. LAMIS selects the most representative instances, satisfying both objectives: high accuracy and reduction rates. The performance has been evaluated on real world data sets from UCI repository by the ten-fold cross-validation method. The results of experiments have been compared with state-of-the-art methods, where the overall results, show the superiority of the proposed method in terms of classification accuracy and reduction percentage.
Starting Page	25
Ending Page	45
Page Count	21
File Format	PDF
ISSN	18688071
Journal	International Journal of Machine Learning and Cybernetics
Volume Number	7
Issue Number	1
e-ISSN	1868808X
Language	English
Publisher	Springer Berlin Heidelberg
Publisher Date	2014-02-24
Publisher Place	Berlin, Heidelberg
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Instance selection Instance-based classifiers Large symmetric margin Filled function algorithm k-Nearest neighbor (k-NN) Computational Intelligence Artificial Intelligence (incl. Robotics) Control, Robotics, Mechatronics Statistical Physics, Dynamical Systems and Complexity Systems Biology Pattern Recognition
Content Type	Text
Resource Type	Article
Subject	Artificial Intelligence Computer Vision and Pattern Recognition Software

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