Classification of incomplete instance is a challenging problem due to the missing features
generally cause uncertainty in the classification result. A new evidential classification …

The classification analysis of missing data is still a challenging task since the training
patterns may be insufficient and incomplete in many fields. To train a high-performance …

Many instance selection algorithms have been introduced to reduce the high storage
requirements and computation complexity of K-nearest neighbor (K-NN) classification rules …

In pattern classification problem, different classifiers learnt using different training data can
provide more or less complementary knowledge, and the combination of classifiers is …

In practice, the data collected from data mining usually contain some missing values.
Imputation is the process of replacing the missing values in incomplete datasets. It is usually …

Most existing machine learning techniques require complete data. However, incomplete
patterns are common in many real-world scenarios due to the missing values (MVs). Various …

The classification of incomplete patterns is a very challenging task because the object
(incomplete pattern) with different possible estimations of missing values may yield distinct …

In complex incomplete pattern classification, the classification results produced by a single
classifier and used for decision-making may be quite unreliable and uncertain due to the …

Various studies have focused on the classification of uncertain or imbalanced data.
However, previous studies rarely consider the classification for uncertain imbalanced data …

Missing values are a common issue in many real-world datasets, and therefore coping with
such datasets is an essential requirement of classification since inadequate treatment of …