Recently, we have witnessed an explosive growth in both the quantity and dimension of data
generated, which aggravates the high dimensionality challenge in tasks such as predictive …

The recent developments in the modern data collection tools, techniques, and storage
capabilities are leading towards huge volume of data. The dimensions of data indicate the …

Medical decision-making with high-dimensional complex data has recently become a focus
and difficulty in artificial intelligence and the medical field. Tumor diagnosis using data …

This paper studies empirically the effect of sampling and threshold-moving in training cost-
sensitive neural networks. Both oversampling and undersampling are considered. These …

The identification of drug-target interactions has great significance for pharmaceutical
scientific research. Since traditional experimental methods identifying drug-target …

We propose a new method for estimating intrinsic dimension of a dataset derived by
applying the principle of maximum likelihood to the distances between close neighbors. We …

When performing visualization and classification, people often confront the problem of
dimensionality reduction. Isomap is one of the most promising nonlinear dimensionality …

Condition monitoring and fault diagnosis of wind turbines is an attractive yet challenging
task. This paper presents a novel data-driven fault diagnosis scheme for wind turbines …

The ever-growing amount of data stored in digital databases raises the question of how to
organize and extract useful knowledge. This paper outlines some current developments in …

Graph-embedding along with its linearization and kernelization provides a general
framework that unifies most traditional dimensionality reduction algorithms. From this …