Abstract—Human Activity Recognition (HAR) is one of the
main research fields in pattern recognition. In recent years,
machine learning and deep learning have played important
roles in Artificial Intelligence (AI) fields, and are proven to be
very successful in classification tasks of HAR. However, there
are two drawbacks of the mainstream frameworks: 1) all
inputs are processed with the same parameters, which would
cause the framework to incorrectly assign an unrealistic label
to the object; 2) these frameworks lack generality in different
application scenarios. In this paper, an adaptive multi-state
pipe framework based on Set Pair Analysis (SPA) is presented,
where pipes are mainly divided into three kinds of types: main
pipe, sub-pipe and fusion pipe. In the main pipe, the input of
classification tasks is preprocessed by SPA to obtain the
Membership Belief Matrix (MBM). The sub-pipe shunt
processing is performed according to the membership belief.
The results are merged through the fusion pipe in the end. To
test the performance of the proposed framework, we attempt to
find the best configuration set that yields the optimal
performance and evaluate the effectiveness of the new
approach on the popular benchmark dataset WISDM.
Experimental results demonstrate that the proposed
framework can get the good performance by achieving a result
of 1.4% test error.
Index Terms—Human activity recognition (HAR),
membership belief matrix (MBM), multi-state pipe framework,
set pair analysis (SPA).
The authors are with the School of Information Science and Engineering,
Shandong University, Qingdao, China (e-mail: sduslx@163.com,
hongjixu@sdu.edu.cn).
Cite: Leixin Shi, Hongji Xu, Beibei Zhang, Xiaojie Sun, Juan Li, and Shidi Fan, "Adaptive Multi-state Pipe Framework Based on Set Pair Analysis," International Journal of Machine Learning and Computing vol. 11, no. 1, pp. 158-163, 2021.
Copyright © 2021 by the authors. This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).