Abstract Multiple Classifier Systems (MCS) have been widely studied as an alternative for
increasing accuracy in pattern recognition. One of the most promising MCS approaches is …

Classifier learning with data-sets that suffer from imbalanced class distributions is a
challenging problem in data mining community. This issue occurs when the number of …

Data collection is a major bottleneck in machine learning and an active research topic in
multiple communities. There are largely two reasons data collection has recently become a …

Current benchmark reports of classification algorithms generally concern common classifiers
and their variants but do not include many algorithms that have been introduced in recent …

Discretization is an essential preprocessing technique used in many knowledge discovery
and data mining tasks. Its main goal is to transform a set of continuous attributes into discrete …

Imbalanced data is a common problem in classification. This phenomenon is growing in
importance since it appears in most real domains. It has special relevance to highly …

Cross-validation is a very commonly employed technique used to evaluate classifier
performance. However, it can potentially introduce dataset shift, a harmful factor that is often …

Subgroup discovery is a data mining technique which extracts interesting rules with respect
to a target variable. An important characteristic of this task is the combination of predictive …

In many practical applications of classifiers, not only high accuracy but also high
interpretability is required. Among a wide variety of existing classifiers, Takagi–Sugeno …

Broad learning system (BLS) is a novel and efficient model, which facilitates representation
learning and classification by concatenating feature nodes and enhancement nodes. In spite …