The low prediction accuracy problem in learning
Honghua Dai · 2002
Achieving a higher prediction accuracy rate is crucial for all learning algorithms, particularly for real application purposes. This paper presents the factors which could prevent a learning algorithm from achieving a higher prediction accuracy rate, and indicates that overfitting on low-quality data and being misled by this are two important factors. It also presents strategies for dealing with this problem. A new approach, called field learning, is described, by which the learnt rules can overcome this problem and achieve a higher prediction accuracy on new unseen cases. Our experiments show that this approach can achieve a higher prediction accuracy rate on new unseen cases, but it achieved a lower accuracy rate on some of the training data sets.>