The artificial intelligence method was used to optimize an early cancer detection test to ensure high sensitivity and specificity.

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 ...

The visual data mining process, seen in the first part of this two-part article, revealed patterns in four dimensions between cumulative gas well production and independent variables ...

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 ...

Unlike conventional black-box AI models that flag anomalies without explanation, IFAT produces decision trees that map the ...

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

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 ...

"NeuralTree benefits from the accuracy of a neural network and the hardware efficiency of a decision tree algorithm," Shoaran says. "It's the first time we've been able to integrate such a complex ...

There are many other techniques for binary classification, but using a decision tree is very common and the technique is considered a fundamental machine learning skill for data scientists. There are ...