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Missing data imputation is a key issue in learning from incomplete data. Various techniques have been developed with great successes on dealing with missing values in data sets with homogeneous attributes (their independent attributes are all either continuous or discrete). This paper studies a new setting of missing data imputation, i.e., imputing missing data in data sets with heterogeneous attributes...
kNN is a simple, but effective and powerful lazy learning algorithm. It has been now widely used in practice and plays an important role in pattern classification. However, how to choose an optimal value of k is still a challenge, which straightforwardly affects the performance of kNN. To alleviate this problem, in this paper we propose a new learning algorithm under the framework of kNN. The primary...
In supervised learning, missing values usually appear in the training set. The missing values in a dataset may generate bias, affecting the quality of the supervised learning process or the performance of classification algorithms. These imply that a reliable method for dealing with missing values is necessary. In this paper, we analyze the difference between iterative imputation of missing values...
In many real world data mining and classification tasks, we face with the problem of high cost in making training data sets. In addition, in many domains, different misclassification errors involve different costs. These two issues are often addressed by semi-supervised learning and cost-sensitive learning separately. Sometimes the two issues can happen at the same time in real world applications...
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