Feature selection plays an important role in the classification problems associated with
expert and intelligent systems. The central idea behind feature selection is to identify …

Neighborhood rough sets provide important insights into dealing with numerical data.
Neighborhood radius, a key factor that affects data uncertainty, is uniformly given in most of …

Feature selection is one of the core contents of rough set theory and application. Since the
reduction ability and classification performance of many feature selection algorithms based …

Of all of the challenges which face the effective application of computational intelligence
technologies for pattern recognition, dataset dimensionality is undoubtedly one of the …

Aiming at the problem of effectively selecting relevant features from heterogeneous data
without decision, a novel feature selection approach is studied based on fuzzy mutual …

Abstract We study the Rough Set theory as a method of feature selection based on tolerant
classes that extends the existing equivalent classes. The determination of initial tolerant …

Practical machine learning algorithms are known to degrade in performance (prediction
accuracy) when faced with many features (sometimes attribute is used instead of feature) …

Neighborhood rough set theory is widely used to measure the uncertainty of data in machine
learning and data mining. However, the neighborhood radius has a significant influence on …

Fuzzy rough set method provides an effective approach to data mining and knowledge
discovery from hybrid data including categorical values and numerical values. However, its …

Rough set theory is one of the effective methods to feature selection which can preserve the
characteristics of the original features by deleting redundant information. The main idea of …