Neural networks (NNs) and decision trees (DTs) are both popular models of machine
learning, yet coming with mutually exclusive advantages and limitations. To bring the best of …

Finding information hidden in data is as theoretically difficult as it is practically important.
With the objective of discovering unknown patterns from data, the methodologies of data …

Neural networks (NNs) and decision trees (DTs) are both popular models of machine
learning, yet coming with mutually exclusive advantages and limitations. To bring the best of …

There is a growing desire in the field of reinforcement learning (and machine learning in
general) to move from black-box models toward more" interpretable AI." We improve …

Currently, tacit knowledge has attracted increasing research attention. However, the
theoretical foundation of tacit knowledge is still not well formulated, because the researches …

Mimicking the human brain to achieve human-level cognition performance has been a core
challenge in artificial intelligence research for decades. Humans are very efficient in …

Available data for a large number of AB2 compounds were subjected to a rigorous study
using a combination of Principal Component Analysis (PCA) technique, multiobjective …

This paper presents the design of a hybrid learning model, termed as neural network tree
(NNTree). It incorporates the advantages of both decision tree and neural network. An …

Decision trees (DT) are a type of data classifiers. A typical classifier works in two phases. In
the first, the learning phase, the classifier is built according to a preexisting data (training) …

In autonomous driving, predicting the behavior (turning left, stopping, etc.) of target vehicles
is crucial for the self-driving vehicle to make safe decisions and avoid accidents. Existing …