Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Conditional Synthetic Data Generation for Personal Thermal Comfort
  Models   Personal thermal comfort models aim to predict an individual's thermal
comfort response, instead of the average response of a large group. Recently,
machine learning algorithms have proven to be having enormous potential as a
candidate for personal thermal comfort models. But, often within the normal
settings of a building, personal thermal comfort data obtained via experiments
are heavily class-imbalanced. There are a disproportionately high number of
data samples for the "Prefer No Change" class, as compared with the "Prefer
Warmer" and "Prefer Cooler" classes. Machine learning algorithms trained on
such class-imbalanced data perform sub-optimally when deployed in the real
world. To develop robust machine learning-based applications using the above
class-imbalanced data, as well as for privacy-preserving data sharing, we
propose to implement a state-of-the-art conditional synthetic data generator to
generate synthetic data corresponding to the low-frequency classes. Via
experiments, we show that the synthetic data generated has a distribution that
mimics the real data distribution. The proposed method can be extended for use
by other smart building datasets/use-cases.

25
The categories of this paper are: patt-sol
35
The categories of this paper are: mtrl-th
7
The categories of this paper are: hep-th
18
The categories of this paper are: econ
4
The categories of this paper are: cond-mat
9
The categories of this paper are: nlin
11
The categories of this paper are: hep-lat
22
The categories of this paper are: chao-dyn
32
The categories of this paper are: atom-ph
29
The categories of this paper are: funct-an
20
The categories of this paper are: acc-phys
8
The categories of this paper are: hep-ex
23
The categories of this paper are: solv-int
16
The categories of this paper are: stat
37
The categories of this paper are: supr-con
21
The categories of this paper are: adap-org