Global vs local classification models for multi-sensor data fusion

Date

2018

Authors

Pippa, E.
Zacharaki, E. I.
Özdemir, A. T.
Barshan, Billur
Megalooikonomou, V.

Editor(s)

Advisor

Supervisor

Co-Advisor

Co-Supervisor

Instructor

BUIR Usage Stats
3
views
26
downloads

Citation Stats

Series

Abstract

The aim of this paper is to investigate feature extraction and fusion of information across a number of sensors in different spatial locations to classify temporal events. Although the common feature-level fusion allows capturing spatial dependencies across sensors, the significant increase of feature vector dimensionality does not allow learning the classification models using a small number of samples usually available in practice. In decision-level fusion on the other hand, sensor-specific classification models are trained and subsequently integrated to reach a combined decision. Recent work has shown that decision-level fusion with a global (common for all sensors) classification model, is more appropriate for generalized events that show a (weak or strong) manifestation across all sensors. Although we can hypothesize that the choice of scheme depends on the event type (generalized vs focal/local), the prior work does not provide enough evidence to guide on the choice of fusion scheme. Thus in this work we aim to compare the three data fusion schemes for classification of generalized and non-generalized events using two case scenarios: (i) classification of paroxysmal events based on EEG patterns and (ii) classification of falls and activities of daily living (ADLs) from multiple sensors. The results support our hypothesis that feature level fusion is more beneficial for the characterization of heterogeneous data (based on an adequate number of samples), while sensor-independent classifiers should be selected in the case of generalized manifestation patterns.

Source Title

SETN '18 Proceedings of the 10th Hellenic Conference on Artificial Intelligence

Publisher

ACM

Course

Other identifiers

Book Title

Degree Discipline

Degree Level

Degree Name

Citation

Published Version (Please cite this version)

Language

English

Type

Conference Paper