Empirical learning aided by weak domain knowledge in the form of feature importance

Iqbal, Ridwan Al (2010) Empirical learning aided by weak domain knowledge in the form of feature importance. [Preprint] (Unpublished)

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Standard hybrid learners that use domain knowledge require stronger knowledge that is hard and expensive to acquire. However, weaker domain knowledge can benefit from prior knowledge while being cost effective. Weak knowledge in the form of feature relative importance (FRI) is presented and explained. Feature relative importance is a real valued approximation of a feature’s importance provided by experts. Advantage of using this knowledge is demonstrated by IANN, a modified multilayer neural network algorithm. IANN is a very simple modification of standard neural network algorithm but attains significant performance gains. Experimental results in the field of molecular biology show higher performance over other empirical learning algorithms including standard backpropagation and support vector machines. IANN performance is even comparable to a theory refinement system KBANN that uses stronger domain knowledge. This shows Feature relative importance can improve performance of existing empirical learning algorithms significantly with minimal effort.

Item Type:Preprint
Keywords:neural network, domain knowledge, prior knowledge, feature importance
Subjects:Computer Science > Machine Learning
ID Code:6855
Deposited By: Iqbal, Ridwan Al
Deposited On:06 Jun 2010 14:35
Last Modified:11 Mar 2011 08:57

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