"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 ...

For the decision tree presented in this article, categorical predictors, such as sex, State and political leaning, should be zero-based integer-encoded. Because the tree-building algorithm computes ...

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

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 ...

Boosted decision trees Physicists have been using decision trees since the 1970s. Decision-tree algorithms work by running data through a series of decision points. At each point, the algorithm ...

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 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.

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

"Jump discontinuities" in visual plots led to use of data mining decision trees as an ideal form of analysis useful in obtaining a profit exploration pattern from the British Columbia database.