Source Themes

Using knockoffs for controlled predictive biomarker identification

One of the key challenges of personalized medicine is to identify which patients will respond positively to a given treatment. The area of subgroup identification focuses on this challenge, that is, identifying groups of patients that experience …

A machine learning perspective on the emotional content of Parkinsonian speech

Patients with Parkinson's disease (PD) have distinctive voice patterns, often perceived as expressing sad emotion. While this characteristic of Parkinsonian speech has been supported through the perspective of listeners, where both PD and healthy …

When Size Matters: Markov Blanket with Limited Bit Depth Conditional Mutual Information

Due to the proliferation of mobile computing and Internet of Things devices, there is an urgent need to push the machine learning frontiers to the network edge so as to fully unleash the potential of the edge big data. Since feature selection becomes …

Feature selection with limited bit depth mutual information for portable embedded systems

Since wearable computing systems have grown in importance in the last years, there is an increased interest in implementing machine learning algorithms with reduced precision parameters/computations. Not only learning, also feature selection, most of …

Multi-target regression via output space quantization

Multi-target regression is concerned with the prediction of multiple continuous target variables using a shared set of predictors. Two key challenges in multi-target regression are: (a) modelling target dependencies and (b) scalability to large …

Efficient feature selection using shrinkage estimators

Information theoretic feature selection methods quantify the importance of each feature by estimating mutual information terms to capture: the relevancy, the redundancy and the complementarity. These terms are commonly estimated by maximum …

On the Stability of Feature Selection in the Presence of Feature Correlations

Feature selection is central to modern data science. The ‘stability’ of a feature selection algorithm refers to the sensitivity of its choices to small changes in training data. This is, in effect, the robustness of the chosen features. This paper …

Information Theoretic Multi-Target Feature Selection via Output Space Quantization

A key challenge in information theoretic feature selection is to estimate mutual information expressions that capture three desirable terms—the relevancy of a feature with the output, the redundancy and the complementarity between groups of features. …

Insights into distributed feature ranking

In an era in which the volume and complexity of datasets is continuously growing, feature selection techniques have become indispensable to extract useful information from huge amounts of data. However, existing algorithms may not scale well when …

Multi-target feature selection through output space clustering

A key challenge in information theoretic feature selection is to estimate mutual information expressions that capture three desirable terms: the relevancy of a feature with the output, the redundancy and the complementarity between groups of …