We discuss a novel decision tree architecture with soft decisions at the internal nodes where
we choose both children with probabilities given by a sigmoid gating function. Our algorithm …

Decision trees are attractive classifiers due to their high execution speed. But trees derived
with traditional methods often cannot be grown to arbitrary complexity for possible loss of …

The algorithm on Decision tree is the most widely used method of inductive inference, and it
is a simple method of knowledge representation, Different examples can be divided into …

Some data mining problems require predictive models to be not only accurate but also
comprehensible. Comprehensibility enables human inspection and understanding of the …

Data mining techniques are a rapidly emerging class of applications that have widespread
use in several fields. One important problem in data mining is classification, which is the task …

Much of previous attention on decision trees focuses on the splitting criteria and optimization
of tree sizes. The dilemma between overfitting and achieving maximum accuracy is seldom …

There has been increasing number of independently proposed randomization methods in
different stages of decision tree construction to build multiple trees. Randomized decision …

The paper compares the classification performance rate of eight models: logistic regression
(LR), neural network (NN), radial basis function neural network (RBFNN), support vector …

A decision tree is an important classification technique in data mining classification.
Decision trees have proved to be valuable tools for the classification, description, and …

This paper discusses various techniques for soft decision making by neural networks.
Decision making problems are described as choosing an action from possible alternatives …