Real-world problems are commonly characterized by a high feature dimensionality, which
hinders the modelling and descriptive analysis of the data. However, some of these data …

Feature selection (FS) is a popular data pre-processing technique in machine learning to
extract the optimal features to maintain or increase the classification accuracy of the dataset …

Outliers carry significant information to reflect an anomaly mechanism, so outlier detection
facilitates relevant data mining. In terms of outlier detection, the classical approaches from …

Multi-label feature selection is an indispensable technology in multi-semantic high-
dimensional data preprocessing, which has been brought into focus in recent years …

Multi-source information fusion (MSIF) is a useful strategy for combining complimentary data
from numerous information sources to produce an overall precise description, which can …

Rough set theories are utilized in class-specific feature selection to improve the
classification performance of continuous data while handling data uncertainty. However …

As a fundamental granular computing strategy, neighborhood granulation has been
acknowledged as an intuitive and effective approach to feature evaluation and selection …

Feature selection is a significant preprocessing technique for data mining, which can
promote the accuracy of data classification and shrink feature space by eliminating …

Intelligent forecasting model of stock price is an effective way to obtain ideal investment
returns. Due to the impact of quantitative transactions, traditional forecasting methods face …

Attribute reduction is nowadays an extremely important data preprocessing technique in the
field of data mining, which has gained much attention due to its ability to provide better …