Show/Hide Menu
Hide/Show Apps
anonymousUser
Logout
Türkçe
Türkçe
Search
Search
Login
Login
OpenMETU
OpenMETU
About
About
Açık Bilim Politikası
Açık Bilim Politikası
Frequently Asked Questions
Frequently Asked Questions
Browse
Browse
By Issue Date
By Issue Date
Authors
Authors
Titles
Titles
Subjects
Subjects
Communities & Collections
Communities & Collections
Reducing Features to Improve Link Prediction Performance in Location Based Social Networks, Non-Monotonically Selected Subset from Feature Clusters
Date
2019-01-01
Author
Bayrak, Ahmet Engin
Polat, Faruk
Metadata
Show full item record
This work is licensed under a
Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License
.
Item Usage Stats
10
views
0
downloads
In most cases, feature sets available for machine learning algorithms require a feature engineering approach to pick the subset for optimal performance. During our link prediction research, we had observed the same challenge for features of Location Based Social Networks (LBSNs). We applied multiple reduction approaches to avoid performance issues caused by redundancy and relevance interactions between features. One of the approaches was the custom two-step method; starts with clustering features based on the proposed interaction related similarity measurement and ends with non-monotonically selecting optimal feature subset from those clusters. In this study, we applied well-known generic feature reduction algorithms together with our custom method for LBSNs to evaluate novelty and verify the contributions. Results from multiple data groups depict that our custom feature reduction approach makes higher and more stable effectivity optimizations for link prediction when compared with others.
Subject Keywords
Feature Reduction
,
Social Networks
,
Location Based Social Networks
,
Link Prediction
URI
https://hdl.handle.net/11511/40730
DOI
https://doi.org/10.1145/3341161.3343853
Collections
Department of Computer Engineering, Conference / Seminar