Cardiovascular diseases are the primary reason for mortality worldwide. As per WHO survey
report in 2019, 17.9 million people died due to CVDs, accounting for 32% of all global …

Feature selection can effectively define the feature subset, remove redundant, irrelevant,
and noisy features. In order to adapt the feature selection problem, this paper adopts role …

Knowledge discovery of heterogeneous data is an active topic in knowledge engineering.
Feature selection for heterogeneous data is an important part of effective data analysis …

Most extant feature selection works neglect interactive features in the form of groups, leading
to the omission of some important discriminative information. Moreover, the prevalence of …

The machine learning based techniques for detection of lungs cancer can assist the
clinicians in assessing the risk of pulmonary nodules being malignant. We are developing …

Feature selection is an effective data preprocessing technique that can effectively alleviate
the curse of dimensionality in multi-label learning. The technique selects a subset of features …

Streaming feature selection (SFS), is the task of selecting the most informative features in
dealing with high-dimensional or incrementally growing problems. Several SFS algorithms …

Damage detection techniques have been widely explored over the last years driven by the
advances of computational intelligence technologies. To understand the structure's dynamic …

Estimating interest rates is a typical multivariate prediction problem that has garnered
considerable attention in the finance industry. However, the rising complexity of the …

Dynamic multiobjective optimization poses great challenges to evolutionary algorithms due
to the change of optimal solutions or Pareto front with time. Learning-based methods are …