Selective knowledge transfer from K-nearest neighbour tasks
LE3 .A278 2005
2005
Silver, Danny
Acadia University
Bachelor of Computer Science
Honours
Computer Science
The thesis explores how a learning system can utilize previously learned knowledge to develop a more accurate hypothesis in the context of the k nearest neighbour (kNN) learning algorithm. Several previous methods of knowledge transfer for kNN have proposed measures based on structural similarity at the task level. A theory of selective knowledge transfer is presented using a measure of relatedness based on functional similarity at the classification level. The new method of knowledge transfer relies on the generation of virtual instances for the primary task from training instances of the secondary task. Each virtual instance is non-deterministic in that the probability of its class value is conditioned upon the class value of the secondary task. Virtual-instance-based Non-Deterministic kNN (VND-kNN) is introduced as an implementation of the theory. A prototype system based on the theory is tested against a synthetic domain and a letter recognition domain. Experiments show that knowledge transfer from secondary tasks based on the conditional probability distributions can improve the generalization accuracy of the primary task if the secondary tasks are related to the primary task. Furthermore, experiments show that the method is able to mitigate negative transfer of knowledge when the secondary tasks are unrelated to the primary task.
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https://scholar.acadiau.ca/islandora/object/theses:478
