Title
Improvement for action strategy learning in classification task using classification probalilities
Abstract
In this paper, we address the autonomous evidence accumulation when a system classifies an object into one of predetermined categories. We propose a reinforcement learning system that effectively selects actions to speed up the classification process. The proposed system accelerates its learning using classification probabilities calculated by a classification system. We conducted three binary classification experiments to evaluate the learning speed and correctness of the proposed system. In the first experiment, we examined a random action selection strategy that does not learn its selection parameters while accumulating evidence. In the second experiment, we examined Paletta's reinforcement learning system that observes the state of the object and learns action selection strategy. In the third experiment, we examined the proposed system that observes both the object state and the classification probability. The proposed system showed the fastest learning.
Year
DOI
Venue
2012
10.1109/SCIS-ISIS.2012.6505012
SCIS&ISIS
Keywords
Field
DocType
learning (artificial intelligence),pattern classification,probability,paletta system,action strategy learning,binary classification experiment,classification probalility,classification process,classification task,evidence accumulation,learning correctness,learning speed,random action selection strategy,selection parameter
One-class classification,Semi-supervised learning,Binary classification,Computer science,Artificial intelligence,Linear classifier,Action selection,Machine learning,Learning classifier system,Reinforcement learning,Multiclass classification
Conference
ISSN
ISBN
Citations 
2377-6870
978-1-4673-2742-8
0
PageRank 
References 
Authors
0.34
3
4
Name
Order
Citations
PageRank
Chyon Hae Kim13011.81
Yamazaki, S.220.72
Tsujino, H.3151.38
S. Sugano4403.71