Gradient boosting machines are a family of powerful machine-learning techniques that have
shown considerable success in a wide range of practical applications. They are highly …

A knowledge graph (KG), a special form of semantic network, integrates fragmentary data
into a graph to support knowledge processing and reasoning. KG quality control is important …

Ensemble methods that train multiple learners and then combine them to use, with Boosting
and Bagging as representatives, are well-known machine learning approaches. It has …

There is no doubt that similarity is a fundamental notion in the field of machine learning and
pattern recognition. How to represent and measure similarity appropriately is a pursuit of …

Ensemble learning, as a popular method to tackle concept drift in data stream, forms a
combination of base classifiers according to their global performances. However, concept …

By efficiently building and exploiting surrogates, data-driven evolutionary algorithms
(DDEAs) can be very helpful in solving expensive and computationally intensive problems …

In solving many real-world optimization problems, neither mathematical functions nor
numerical simulations are available for evaluating the quality of candidate solutions. Instead …

In classification, machine learning algorithms can suffer a performance bias when data sets
are unbalanced. Data sets are unbalanced when at least one class is represented by only a …

In a DNA microarray dataset, gene expression data often has a huge number of features
(which are referred to as genes) versus a small size of samples. With the development of …

Diversity among individual classifiers is recognized to play a key role in ensemble, however,
few theoretical properties are known for classification. In this paper, by focusing on the …