  Different features have different relevance to a particular learning problem.
Some features are less relevant; while some very important. Instead of
selecting the most relevant features using feature selection, an algorithm can
be given this knowledge of feature importance based on expert opinion or prior
learning. Learning can be faster and more accurate if learners take feature
importance into account. Correlation aided Neural Networks (CANN) is presented
which is such an algorithm. CANN treats feature importance as the correlation
coefficient between the target attribute and the features. CANN modifies normal
feed-forward Neural Network to fit both correlation values and training data.
Empirical evaluation shows that CANN is faster and more accurate than applying
the two step approach of feature selection and then using normal learning
algorithms.

Machine Learning;Artificial Intelligence;Computer Vision and Pattern Recognition