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

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This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

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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

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Citation Formats

A. E. Bayrak and F. Polat, “Reducing Features to Improve Link Prediction Performance in Location Based Social Networks, Non-Monotonically Selected Subset from Feature Clusters,” 2019, Accessed: 00, 2020. [Online]. Available: https://hdl.handle.net/11511/40730.