Feature selection for high-dimensional temporal data

Feature Selection for Small Sample Sets with High Dimensional Data Using Heuristic Hybrid Approach

Feature selection can significantly be decisive when analyzing high dimensional data, especially with a small number of samples. Feature extraction methods do not have decent performance in these conditions. With small sample sets and high dimensional data, exploring a large search space and learning from insufficient samples becomes extremely hard. As a result, neural networks and clustering a...

Feature selection for high-dimensional temporal data: supplementary material

Feature Selection for High-dimensional Integrated Data

Motivated by the problem of identifying correlations between genes or features of two related biological systems, we propose a model of feature selection in which only a subset of the predictors Xt are dependent on the multidimensional variate Y , and the remainder of the predictors constitute a “noise set” Xu independent of Y . Using Monte Carlo simulations, we investigated the relative perfor...

Feature selection for high-dimensional industrial data

In the semiconductor industry the number of circuits per chip is still drastically increasing. This fact and strong competition lead to the particular importance of quality control and quality assurance. As a result a vast amount of data is recorded during the fabrication process, which is very complex in structure and massively affected by noise. The evaluation of this data is a vital task to ...

T-Relief: Feature Selection for Temporal High- Dimensional Gene Expression Data

The high-dimensionality of microarray datasets with a small number of samples presents a challenge for the microarray data classification task, especially when features vary through time. Feature selection has been widely used in data mining and machine learning as a preprocessing step to reduce number of features and to enhance classification performance. Even though various feature selection ...