This paper presents a new class of local similarity metrics, called AASM, that are not symmetric and that can be adopted as the basic retrieval method in a CBR system. An anytime learning procedure is also introduced that, starting from an initial set of stored cases, improves the retrieval accuracy by modifying the local definition of the metric. The learning procedure is a reinforcement learning algorithm and can be run as a black box since no particular setting is required. With the aid of classical test sets it is shown that AASM can improve in many cases the accuracy of both nearest neighbour methods and Salzberg’s NGE. Moreover, AASM can achieve significant data compression (10%) while maintaining the same accuracy as NN
Learning a Local Similarity Metric for Case-Based Reasoning
Ricci, Francesco;Avesani, Paolo
1995-01-01
Abstract
This paper presents a new class of local similarity metrics, called AASM, that are not symmetric and that can be adopted as the basic retrieval method in a CBR system. An anytime learning procedure is also introduced that, starting from an initial set of stored cases, improves the retrieval accuracy by modifying the local definition of the metric. The learning procedure is a reinforcement learning algorithm and can be run as a black box since no particular setting is required. With the aid of classical test sets it is shown that AASM can improve in many cases the accuracy of both nearest neighbour methods and Salzberg’s NGE. Moreover, AASM can achieve significant data compression (10%) while maintaining the same accuracy as NNI documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
