Improving a fuzzy association rule-based classification model by granularity learning based on heuristic measures over multiple granularities

The time or space complexity may considerably increase for a single classifier if all features are taken into account. Thus, it is reasonable to train a single classifier by partial features. Then, a set of multiple classifiers can be generated, and an aggregation of outputs from different classifiers is subsequently performed. The aim of this paper is to propose a classification system with a heuristic fusion scheme in which multiple fuzzy association rule-based classifiers with partial features are combined, and show the feasibility and effectiveness of fusing multiple classifiers through the Sugeno integral extended by ordered weighted averaging operators. In comparison with the Sugeno integral by computer simulations on the iris data and the appendicitis data show that the overall classification accuracy rate could be improved by the Sugeno integral with ordered weighted averaging operators. The experimental results further demonstrate that the proposed method performs well in comparison with other fuzzy or non-fuzzy classification methods.

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Improving medical rule-based expert systems comprehensibility: fuzzy association rule mining approach

International Journal of Artificial Intelligence and Soft Computing ◽

10.1504/ijaisc.2012.048179 ◽

2012 ◽

Vol 3 (1) ◽

pp. 29 ◽

Cited By ~ 4

Author(s):

Olufunke O. Oladipupo ◽

Charles O. Uwadia ◽

Charles K. Ayo

Keyword(s):

Expert Systems ◽

Association Rule ◽

Association Rule Mining ◽

Rule Mining ◽

Rule Based ◽

Fuzzy Association Rule ◽

Fuzzy Association Rule Mining

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A fuzzy association rule-based classifier for imbalanced classification problems

Information Sciences ◽

10.1016/j.ins.2021.07.019 ◽

2021 ◽

Author(s):

J. Sanz ◽

M. Sesma-Sara ◽

H. Bustince

Keyword(s):

Association Rule ◽

Classification Problems ◽

Rule Based ◽

Imbalanced Classification ◽

Fuzzy Association Rule ◽

Rule Based Classifier

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A fuzzy association rule-based knowledge management system for occupational safety and health programs in cold storage facilities

VINE Journal of Information and Knowledge Management Systems ◽

10.1108/vjikms-01-2017-0004 ◽

2018 ◽

Vol 48 (2) ◽

pp. 199-216

Author(s):

Y.P. Tsang ◽

K.L. Choy ◽

P.S. Koo ◽

G.T.S. Ho ◽

C.H. Wu ◽

...

Keyword(s):

Cold Storage ◽

Association Rule ◽

Occupational Safety ◽

Occupational Safety And Health ◽

Knowledge Management System ◽

Rule Based ◽

Content Type ◽

Safety And Health ◽

Fuzzy Association Rule ◽

Storage Facilities

Purpose This paper aims to improve operational efficiency and minimize accident frequency in cold storage facilities through adopting an effective occupational safety and health program. The hidden knowledge can be extracted from the warehousing operations to create the comfortable and safe workplace environment. Design/methodology/approach A fuzzy association rule-based knowledge management system is developed by integrating fuzzy association rule mining (FARM) and rule-based expert system (RES). FARM is used to extract hidden knowledge from real operations to establish the relationship between safety measurement, personal constitution and key performance index measurement. The extracted knowledge is then stored and adopted in the RES to establish an effective occupational and safety program. Afterwards, a case study is conducted to validate the performance of the proposed system. Findings The results indicate that the aforementioned relationship can be built in the form of IF-THEN rules. An appropriate safety and health program can be developed and applied to all workers, so that they can follow instructions to prevent cold induced injuries and also improve the productivity. Practical implications Because of the increasing public consciousness of occupational safety and health, it is important for the workers in cold storage facilities where the ambient temperature is at/below 10°C. The proposed system can address the social problem and promote the importance of occupational safety and health in the society. Originality/value This study contributes to the knowledge management system for improving the occupational safety and operational efficiency in the cold storage facilities.

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