TY - EJOU
AU - Fang, Shu
AU - Huang, Lei
AU - Wan, Yi
AU - Sun, Weize
AU - Xu, Jingxin
TI - Outlier Detection for Water Supply Data Based on Joint Auto-Encoder
T2 - Computers, Materials \& Continua
PY - 2020
VL - 64
IS - 1
SN - 1546-2226
AB - With the development of science and technology, the status of the water
environment has received more and more attention. In this paper, we propose a deep
learning model, named a Joint Auto-Encoder network, to solve the problem of outlier
detection in water supply data. The Joint Auto-Encoder network first expands the size of
training data and extracts the useful features from the input data, and then reconstructs
the input data effectively into an output. The outliers are detected based on the network’s
reconstruction errors, with a larger reconstruction error indicating a higher rate to be an
outlier. For water supply data, there are mainly two types of outliers: outliers with large
values and those with values closed to zero. We set two separate thresholds, τ1 and τ2,
for the reconstruction errors to detect the two types of outliers respectively. The data
samples with reconstruction errors exceeding the thresholds are voted to be outliers. The
two thresholds can be calculated by the classification confusion matrix and the receiver
operating characteristic (ROC) curve. We have also performed comparisons between the
Joint Auto-Encoder and the vanilla Auto-Encoder in this paper on both the synthesis data
set and the MNIST data set. As a result, our model has proved to outperform the vanilla
Auto-Encoder and some other outlier detection approaches with the recall rate of 98.94
percent in water supply data.
KW - Water supply data
KW - outlier detection
KW - auto-encoder
KW - deep learning
DO - 10.32604/cmc.2020.010066