Using Feature Weights to Improve Performance of Neural Networks

Iqbal, Ridwan Al (2011) Using Feature Weights to Improve Performance of Neural Networks. [Preprint]

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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.

Item Type:Preprint
Keywords:Feature weight, Feature ranking,Hybrid Learning,Correlation,Constraint learning
Subjects:Computer Science > Artificial Intelligence
Computer Science > Machine Learning
Computer Science > Neural Nets
ID Code:7179
Deposited By: Iqbal, Ridwan Al
Deposited On:16 Feb 2011 19:49
Last Modified:11 Mar 2011 08:57

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