Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Easy Ensemble: Simple Deep Ensemble Learning for Sensor-Based Human
  Activity Recognition   Sensor-based human activity recognition (HAR) is a paramount technology in
the Internet of Things services. HAR using representation learning, which
automatically learns a feature representation from raw data, is the mainstream
method because it is difficult to interpret relevant information from raw
sensor data to design meaningful features. Ensemble learning is a robust
approach to improve generalization performance; however, deep ensemble learning
requires various procedures, such as data partitioning and training multiple
models, which are time-consuming and computationally expensive. In this study,
we propose Easy Ensemble (EE) for HAR, which enables the easy implementation of
deep ensemble learning in a single model. In addition, we propose input masking
as a method for diversifying the input for EE. Experiments on a benchmark
dataset for HAR demonstrated the effectiveness of EE and input masking and
their characteristics compared with conventional ensemble learning methods.

8
The categories of this paper are: hep-ex
13
The categories of this paper are: quant-ph
36
The categories of this paper are: cmp-lg
4
The categories of this paper are: cond-mat
10
The categories of this paper are: q-bio
5
The categories of this paper are: gr-qc
27
The categories of this paper are: comp-gas
14
The categories of this paper are: math-ph
2
The categories of this paper are: math
34
The categories of this paper are: chem-ph
29
The categories of this paper are: funct-an
21
The categories of this paper are: adap-org
25
The categories of this paper are: patt-sol
37
The categories of this paper are: supr-con
33
The categories of this paper are: plasm-ph
16
The categories of this paper are: stat