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Study on a confidence machine learning method based on ensemble learning
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DOI：10.1007/s10586-017-1085-z.png)
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Confidence machine learning method is an important research content in machine learning area and of significant importance to high-risk machine learning application area. In confidence machine learning, design and calculation of confidence level is a difficulty; nevertheless, the algorithm in this paper enables confidence classification even when specific threshold setting is omitted and confidence level calculation for each example and unknown sample is neglected. Based on ensemble learning structure, this algorithm employs twice one-class classifier to classify binary problems, and with reject option being set, confidence learning of binary classification is performed by means of multi-layer ensemble learning. The algorithm has been validated on eight experimental datasets, such as heart disease and diabetes mellitus, and good results have been achieved. The learning method proposed in this paper can make the confidence machine learning easier and more efficient. In the era of big data, the research of pattern recognition algorithm based on small sample is still of great significance.
Keyword:
Confidence machine
Ensemble learning
Reject option
One class classifier
Machine learning
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