Machine learning (ML) has been instrumental in solving complex problems and significantly advancing different areas of our lives. Decision tree-based methods have gained significant …

Apr 1, 2015 · Decision tree algorithms are extensively used in machine learning field to classify biomedical signals. De-noising and feature extraction methods are also utilized to get higher …

The so-called “preprocessing” approaches modify the training data before the data is fed into a machine learning algorithm; these approaches can also be applied when the learning …

A decision tree is a tree whose internal nodes can be taken as tests (on input data patterns) and whose leaf nodes can be taken as categories (of these patterns). These tests are filtered down …

• Decision Tree: based on game of twenty questions – Apply a series of tests to the input pattern – Each test asks a question: e.g. “is the pattern yellow?”

Apr 8, 2025 · In the decision trees article, we discussed how decision trees model decisions through a tree-like structure, where internal nodes represent feature tests, branches represent …

Feb 27, 2023 · Decision Trees are the foundation for many classical machine learning algorithms like Random Forests, Bagging, and Boosted Decision Trees.

Nov 3, 2020 · In this paper, a decision tree modulation recognition algorithm based on feature extraction and a conventional classifier recognition based on SVM are proposed. 9 kinds of …

Decision Trees are a non-parametric supervised learning method used for both classification and regression tasks. The goal is to create a model that predicts the value of a target variable by …

Decision Trees are a non-parametric supervised learning method used for classification and regression. The goal is to create a model that predicts the value of a target variable by …