Novel algorithms of attribute reduction with variable precision rough set model

A Novel Variable Precision Reduction Approach to Comprehensive Knowledge Systems

“…However, the former is usually much more computationally intensive and is limited to much lower dimension problems than the later, even when using the approach introduced by Yang [41] to minimize the number of elements in the discernibility matrix to decrease the computational load. In the case of multi-reducts and multi-knowledge extraction methods based on the positive region, the related work mainly falls into two categories:…”

Multi-view attribute reduction model for traffic bottleneck analysis

“…Specifically, attribute reduction algorithms are good at processing uncertain data [21,30,36] and efficient to discover the rule-type knowledge from data tables [26,39]. The table-formed information systems are defined as IS ¼ ðU; C; f ; VÞ, in which U is a finite set of data items, called the universe, C is a finite set of attributes to depict items, V denotes the domain of attribute values, and f is the mapping from U to V, which assigns particular attribute values to items.…”

A Two-step Case-based Reasoning Method Based on Attributes Reduction for Predicting the Endpoint Phosphorus Content

“…There have been a lot of algorithms. Kai Zheng 13) proposed an Enhancement for Heuristic Attribute Reduction (EHAR) in rough set; Yanyan Yang 14) developed two algorithms to find all β-distributed reducts based on the algorithm of finding all minimal elements; Jin Qian 15) design a novel structure of key-value pair to speed up the computation of equivalence classes and attribute significance and parallelize the traditional attribute reduction process based on MapReduce mechanism. Chang Chunguang 16) put forward a kind of reduction technique for case attributes based on rough set theory to improve the efficiency of case retrieving in CBR; Li Fenggang 17) proposed the attribute selecting strategy on the basis of the attribute-oriented reduction techniques; Wu Zhengjiang 18) gave an attribute reduction algorithm based on GAs and discernable matrixes which can calculate the weak reduction easily and the weak reduction effectiveness is evaluated through k-nearest neighbor accuracy; Yaoxu 19) and Mao Yong 20) introduced the advantages and disadvantages of several methods attributes selection algorithm.…”