Rapid diagnosis of bacterial pneumonia is crucial for clinical diagnosis and treatment, but traditional methods are time-consuming. The wide application of machine learning techniques in medical ...
Abstract: Decision-tree induction algorithms are widely used in machine learning applications in which the goal is to extract knowledge from data and present it in a graphically intuitive way. The ...
AIFAD stands for Automated Induction of Functions over Algebraic Data Types and is an application written in OCaml that improves decision tree learning by supporting significantly more complex kinds ...
The complexity of heterologous protein production necessitates the careful selection of inducer concentrations and growth conditions for each protein and organism, posing a multidimensional ...
ABSTRACT: Decision tree is an effective supervised learning method for solving classification and regression problems. This article combines the Pearson correlation coefficient with the CART decision ...
Kenny Rogers’ description of a gambler, knowing what to throw away, knowing what to keep, could well apply to U.S. Navy data. “As you’re producing a large vast amount of data, it’s about the ability ...
Abstract: This paper presents a survey of evolutionary algorithms that are designed for decision-tree induction. In this context, most of the paper focuses on approaches that evolve decision trees as ...
Decision trees are popular machine learning models due to their simplicity and ease of interpretation. Node splitting is essential for decision trees to effectively learn from data attributes. Gini ...
Some results have been hidden because they may be inaccessible to you
Show inaccessible results