Wrappers Feature Selection in Alzheimer's Biomarkers Using kNN and SMOTE Oversampling

Using kNN Model for Automatic Feature Selection

This paper proposes a kNN model-based feature selection method aimed at improving the efficiency and effectiveness of the ReliefF method by: (1) using a kNN model as the starter selection, aimed at choosing a set of more meaningful representatives to replace the original data for feature selection; (2) integration of the Heterogeneous Value Difference Metric to handle heterogeneous applications...

Wrappers for Feature Subset Selection

In the feature subset selection problem a learning algorithm is faced with the problem of selecting a relevant subset of features upon which to focus its attention while ignoring the rest To achieve the best possible performance with a particular learning algorithm on a particular training set a feature subset selection method should consider how the algorithm and the training set interact We e...

Oversampling for Imbalanced Learning Based on K-Means and SMOTE

Learning from class-imbalanced data continues to be a common and challenging problem in supervised learning as standard classification algorithms are designed to handle balanced class distributions. While different strategies exist to tackle this problem, methods which generate artificial data to achieve a balanced class distribution are more versatile than modifications to the classification a...

Filters, Wrappers and a Boosting-Based Hybrid for Feature Selection

In this paper, we examine the advantages and disadvantages of filter and wrapper methods for feature selection and propose a new hybrid algorithm that uses boosting and incorporates some of the features of wrapper methods into a fast filter method for feature selection. Empirical results are reported on six real-world datasets from the UCI repository, showing that our hybrid algorithm is compet...

Towards Principled Feature Selection: Relevancy, Filters and Wrappers