A new co-training-style random forest for computer aided diagnosis |
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Authors: | Chao Deng M Zu Guo |
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Affiliation: | (1) School of Computer Science and Technology, Harbin Institute of Technology, Harbin, China;(2) China Mobile Research Institute, Beijing, China |
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Abstract: | Machine learning techniques used in computer aided diagnosis (CAD) systems learn a hypothesis to help the medical experts make a diagnosis in the future. To learn a well-performed hypothesis,
a large amount of expert-diagnosed examples are required, which places a heavy burden on experts. By exploiting large amounts
of undiagnosed examples and the power of ensemble learning, the co-training-style random forest (Co-Forest) releases the burden on the experts and produces well-performed hypotheses. However, the Co-forest may suffer
from a problem common to other co-training-style algorithms, namely, that the unlabeled examples may instead be wrongly-labeled
examples that become accumulated in the training process. This is due to the fact that the limited number of originally-labeled
examples usually produces poor component classifiers, which lack diversity and accuracy. In this paper, a new Co-Forest algorithm
named Co-Forest with Adaptive Data Editing (ADE-Co-Forest) is proposed. Not only does it exploit a specific data-editing technique in order to identify and discard
possibly mislabeled examples throughout the co-labeling iterations, but it also employs an adaptive strategy in order to decide
whether to trigger the editing operation according to different cases. The adaptive strategy combines five pre-conditional
theorems, all of which ensure an iterative reduction of classification error and an increase in the scale of new training
sets under PAC learning theory. Experiments on UCI datasets and an application to small pulmonary nodules detection using
chest CT images show that ADE-Co-Forest can more effectively enhance the performance of a learned hypothesis than Co-Forest
and DE-Co-Forest (Co-Forest with Data Editing but without adaptive strategy). |
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