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Aggregation pheromone metaphor for semi-supervised classification
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DOI：10.1016/j.patcog.2013.01.002.png)
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摘要
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This article presents a novel 'self-training' based semi-supervised classification algorithm using the property of aggregation pheromone found in real ants. The proposed method has no assumption regarding the data distribution and is free from parameters to be set by the user. It can also capture arbitrary shapes of the classes. The proposed algorithm is evaluated with a number of synthetic as well as real life benchmark datasets in terms of accuracy, macro and micro averaged F-1 measures. Results are compared with two supervised and three semi-supervised classification techniques and are statistically validated using paired t-test. Experimental results show the potentiality of the proposed algorithm. (C) 2013 Elsevier Ltd. All rights reserved.
Keyword:
Semi-supervised classification
Self-training
Ant colony
Aggregation pheromone
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IF:
7.6
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1.3W
被引数:
4.5W
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