Data preprocessing and reduction have become essential techniques in current knowledge
discovery scenarios, dominated by increasingly large datasets. These methods aim at …

Feature Selection (FS) plays an important role in learning and classification tasks. Its
objective is to select the relevant and non-redundant features. Considering the huge number …

The forecasting of significant societal events such as civil unrest and economic crisis is an
interesting and challenging problem which requires both timeliness, precision, and …

Feature selection is nowadays an extremely important data mining stage in the field of
machine learning due to the appearance of problems of high dimensionality. In the literature …

Feature selection is a subfield of machine learning focused on reducing the dimensionality
of datasets by performing a computationally intensive process. This work presents Parallel …

Feature selection is a crucial step nowadays in machine learning and data analytics to
remove irrelevant and redundant characteristics and thus to provide fast and reliable …

Multilabel feature selection involves the selection of relevant features from multilabeled
datasets, resulting in improved multilabel learning accuracy. Evolutionary search‐based …

Shapelets are time-series segments effective for classifying time-series instances. Joint
learning of both classifiers and shapelets has been studied in recent years because this …

Streaming feature selection for unlabeled data aims to remove redundant and irrelevant
features from the continuously arriving features without label information. Most existing …

In Real-Time Bidding (RTB) advertising, evaluating the Click-Through Rate (CTR) of a bid
request and an ad is important for bidding strategy optimization on Demand-Side Platforms …