Granular Computing Approach to Two-Way Learning Based on Formal Concept Analysis in Fuzzy Datasets

Abstract

The main task of granular computing (GrC) is about representing, constructing, and processing information granules. Information granules are formalized in many different approaches. Different formal approaches emphasize the same fundamental facet in different ways. In this paper, we propose a novel GrC method of machine learning by using formal concept description of information granules. Based on information granules, the model and mechanism of two-way learning system is constructed in fuzzy datasets. It is addressed about how to train arbitrary fuzzy information granules to become necessary, sufficient, and necessary and sufficient fuzzy information granules. Moreover, an algorithm of the presented approach is established, and the complexity of the algorithm is analyzed carefully. Finally, to interpret and help understand the theories and algorithm, a real-life case study is considered and experimental evaluation is performed by five datasets from the University of California-Irvine, which is valuable for applying these theories to deal with practical issues.

Tacit Knowledge Mining of Formula Based on Double Decision Attribute Concept Lattices

“…In addition, we can get a ferment formula with low comprehensive score: high content of aloe vera, low content of Chinese wolfberry, low content of hawthorn, high content of yam and high content of coix seed from these association rules. And rules 4,5,7,8 are the association rules between each component of the ferment and the decision attribute: "high comprehensive score". Moreover, considering these association rules comprehensively, we can get one kind ferment formula with high comprehensive score: low content of aloe vera, high content of Chinese wolfberry, high content of hawthorn, low content of yam and low content of coix seed.…”

“…Nowadays, it has been successfully applied to a variety of fields such as knowledge discovery, data mining, machine learning and software engineering [4][5][6][7][8][9][10]. With the deepening of the research on FCA, the notion of a decision formal context was introduced by Zhang and Qiu [11].…”

“…Similarly, the object set of low cost:{5,10,11,12,13}, the object set of medium cost:{1, 4,6,8,9,15,16,17,18,20,22,24,25} and the object set of high cost:{2,3,7,14,19,21,23} can be obtained (Figure 2 in Appendix). Correspondingly, the formulas were divided into three categories: low cost (W1), medium cost (W2) and high cost (W3).…”

A comprehensive survey on formal concept analysis, its research trends and applications

“…An information granule is the basic notion of granular computing, which can be defined broadly as a collection of information. This notion has been recently introduced into the concept lattice as an attempt to decrease the computation time (Belohlavek et al, 2013;Wu et al, 2009;Xu and Li, 2015). In general, the information granule regarded as a collection of elements drawn together by their closeness (resemblance, proximity, functionality, etc.)…”

Classification Based on Invariants of the Data Matrix

“…It covers multiple process modeling concepts of information processing in various hierarchical systems, as well as new approaches to learning with fuzzy databases [8] [9]. In this respect, the paradigm has common roots with the methods of machine learning.…”