Learning relational facts from the web: A tolerance rough set approach

Harvesting Patterns from Textual Web Sources with Tolerance Rough Sets

“…FRL results are as reported in Bharadwaj and Ramanna. 14 TPL 1.0 results are as reported in Sengoz and Ramanna 30.…”

“…Figure 5. Zones of Recognition for Contexts Emerging from Approximations of Candidate Instance np iFrom Sengoz and Ramanna 30. …”

Valid Incremental Attribute Reduction Algorithm Based on Attribute Generalization for an Incomplete Information System

“…Rough set theory, which was proposed by the Polish scientist Pawlak Z. in 1982, is a method of data analysis [1] exploited due to its superiority in processing uncertain data. In recent times, this theory has been widely used in several fields, such as artificial intelligence, machine learning, pattern recognition, data mining, intelligent information processing, and granular computing [2][3][4][5][6] .…”

“…Attribute reduction, also known as feature selection, is a vital application of rough set theory in machine learning [1][2][3][4][5][6][7] . In attribute reduction, the method of constructing a superior evaluation function for the attributes set is the focus of the current research [7][8][9][10] .…”

Short Text Classification with Tolerance Near Sets

“…•"( ) However, there are certain applications such as document classification [16] and natural language processing [61,34] where overlapping classes are necessary with a tolerance form of rough sets [64]. A tolerance model uses a tolerance relation instead of equivalence where the transitivity property is relaxed which enables overlapping classes (in other words, objects can belong to more than one class).…”