Conclusions: Multiple molecular and clinicopathological variable integrated decision tree algorithms may individually predict the recurrence pattern for NPC. This decision tree algorism provides a ...

Evolutionary algorithms have emerged as a robust alternative to traditional greedy approaches for decision tree induction. By mimicking the natural selection process, these algorithms iterate over ...

A decision tree is a machine learning technique that can be used for binary classification or multi-class classification. A binary classification problem is one where the goal is to predict the value ...

But there is also some empirical work comparing various algorithms across many datasets and drawing some conclusions, what types of problems tend to do better with trees vs logistic regression.

Advancements in technology, particularly in Artificial Intelligence (AI), are transforming our ability to predict and manage natural disasters. This technology, with its ability to analyze vast ...

The authors’ approach is based on differences between assembly op-code frequencies in malware and benign classes. They have also utilized decision tree algorithms to simplify the classification.

The original risk estimation algorithm and the CART decision tree are shown in Figures 1 and 2, respectively. P P P Results from the comparisons of the 3 classification approaches are shown in ...

Data mining algorithm selection: decision trees The visual data mining process, seen in the first part of this two-part article, revealed patterns in four dimensions between cumulative gas well ...

Decision-tree algorithms work by running data through a series of decision points. At each point, the algorithm decides whether to keep or reject a piece of data based on criteria programmed into the ...