Abstract. We use the concept of conditional mutual information (MI) to approach problems involving the selection of variables in the area of.

We use the concept of conditional mutual information (MI) to approach problems involving the selection of variables in the area of medical diagnosis.

We use the concept of conditional mutual information (MI) to approach problems involving the selection of variables in the area of medical diagnosis.

In this review, we discuss how measures of dependence considered in information theory are used in classification and regression problems to choose predictors ...

In this paper, an effective feature selection technique called mutual information and Monte Carlo based feature selection (MIMCFS) is proposed.

Abstract. We present a unifying framework for information theoretic feature selection, bringing almost two decades of research on heuristic filter criteria ...

Data-Efficient Information-Theoretic. Test Selection. Marianne Mueller, R´omer Rosales, Harald Steck,. Sriram Krishnan, Bharat Rao, Stefan Kramer. ABCDE. FGHIJ.

May 11, 2024 · To address these issues, we propose the Data-Efficient and Robust Task Selection (DERTS) algorithm, which can be incorporated into both gradient ...

Measuring entropy allows us to quantify how much is learned from each generated test input about the behaviors of the program. Within a probabilistic model of ...

Information-theoretic criteria are widely used for model selection in various fields, including physics, engineering, finance, statistics, and data science.