We propose a feature selection method that constructs each new feature by analysis of tight error clusters. This is a greedy, time-efficient forward selection algorithm that itera...
This paper proposes an iterative methodology for real-time robust mosaic topology inference. It tackles the problem of optimal feature selection (optimal sampling) for global estim...
We propose a novel group regularization which we call exclusive lasso. Unlike the group lasso regularizer that assumes covarying variables in groups, the proposed exclusive lasso ...
Although regarded as one of the most successful algorithm to identify predictive features, Relief is quite vulnerable to outliers and noisy features. The recently proposed I-Relief...
In knowledge discovery applications, where new features are to be added, an acquisition policy can help select the features to be acquired based on their relevance and the cost of...