期刊名称：Lecture Notes in Engineering and Computer Science
印刷版ISSN：2078-0958
电子版ISSN：2078-0966
出版年度：2018
卷号：2233&2234
页码：711-714
出版社：Newswood and International Association of Engineers
摘要：To achieve interference-less machine learning,
therefore avoiding the negative impact of excessive noise/outlier
on training and testing accuracy, we establish four fundamental
hypotheses for application in classification and forecasting tasks.
A spatial transformation, such as Density-Based Spatial Clustering
of Applications with Noise (DBSCAN), and a temporal
transformation, such as Empirical Mode Decomposition (EMD),
have shown the potential to form meaningful representations
of the data in classification and forecasting tasks respectively.
Using these hypotheses, the dataset is preprocessed to generate
meta-information. This meta-information is utilized to
guide the model building stage and noise reduction is evident.
Several learning algorithms—for instance, the Constructive
Backpropagation (CBP) for classification and the long shortterm
memory (LSTM) neural network for forecasting—have
been augmented and tested on real-world benchmark datasets
and our results reported in several research proceedings reveal
significant performance enhancement when conditions for these
hypotheses are satisfied. This paper presents an overview of the
techniques and potential areas of application.
