Abstract
Transfer learning (TL) has grown popular in recent years. It is effective to improve the classification accuracy in the target domain by using the training knowledge in the related domain (called source domain). However, the classification of missing data (or incomplete data) is a challenging task for TL because different strategies of imputation may have strong impacts on learning models. To address this problem, we propose credal transfer learning (CTL) with multi-estimation for missing data based on belief function theory by introducing uncertainty and imprecision in data imputation procedure. CTL mainly consists of three steps: Firstly, the query patterns are reasonably mapped into multiple versions in source domain to characterize the uncertainty caused by missing values. Afterwards, the multiple mapping patterns are classified in the source domain to obtain the corresponding outputs with different discounting factors. Finally, the discounted outputs, represented by the basic belief assignments (BBAs), are submitted to a new belief-based fusion system to get the final classification result for the query patterns. Three comparative experiments are given to illustrate the interests and potentials of CTL method.
Highlights
Traditional machine learning algorithms have already achieved great success under the assumption that training and test set are drawn from the same feature space and data distributions [1]
We propose credal transfer learning (CTL) with multi-estimation for missing data based on belief function theory by introducing uncertainty and imprecision in data imputation procedure
We propose a credal transfer learning (CTL) method for missing data, which introduces uncertainty and imprecision while imputing missing values based on the belief function theory
Summary
Traditional machine learning algorithms have already achieved great success under the assumption that training and test set are drawn from the same feature space and data distributions [1]. We propose a credal transfer learning (CTL) method for missing data, which introduces uncertainty and imprecision while imputing missing values based on the belief function theory. CTL first uses observed attributes to estimate multiple mapping patterns in the source domain for each pattern with missing values in the target domain based on KNNs techniques. 1) A multi-estimation strategy in different distribution domains is proposed In this strategy, the unobserved attributes of incomplete patterns are estimated based on observed ones, with an uncertainty degree reasoned by the belief function theory. Because of the uncertainty reasoned by the belief function theory, CTL is able to make the decision more cautiously by considering the imprecision and uncertainty of learning results Such decision making method effectively reduces the error rate in practice, which is justified by experiments on real world data.
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