Optimization of Automation in Fuzzy Decision Rules

Author(s):  
Renuka Arora ◽  
Rishu Bhatia
Author(s):  
Malcolm J. Beynon ◽  
Paul Jones

This chapter considers the soft computing approach called fuzzy decision trees (FDT), a form of classification analysis. The consideration of decision tree analysis in a fuzzy environment brings further interpretability and readability to the constructed ‘if .. then ..’ decision rules. Two sets of FDT analyses are presented, the first on a small example data set, offering a tutorial on the rudiments of one FDT technique. The second FDT analysis considers the investigation of an e-learning database, and the elucidation of the relationship between weekly online activity of students and their final mark on a university course module. Emphasis throughout the chapter is on the visualization of results, including the fuzzification of weekly online activity levels of students and overall performance.


Author(s):  
Malcolm J. Beynon ◽  
Kirsty Park

This chapter employs the fuzzy decision tree classification technique in a series of biological based application problems. With its employment in a fuzzy environment, the results, in the form of fuzzy ‘if .. then ..’ decision rules, bring with them readability and subsequent interpretability. The two contrasting applications considered concern, the age of abalones and the lengths of torpor bouts of hibernating Greater Horseshoe bats. Emphasis is on the visual results presented, including the series of membership functions used to construct the linguistic variables representing the considered attributes and the final fuzzy decision trees constructed. Technical details presented further offer the opportunity to readers to future employ the technique in other biological applications.


Author(s):  
Александр Владимирович Быков ◽  
Николай Алексеевич Кореневский ◽  
Артем Викторович Винников ◽  
Александр Иванович Безуглов

Целью исследования является разработка метода прогнозирования возникновения и развития тромботических осложнений (тромботических прецедентов), провоцируемых действием новой коронавирусной инфекции (COVID-19) на организм человека, позволяющего усовершенствовать лечебно-диагностические мероприятия для пациентов с данной патологией. В качестве базового математического аппарата была выбрана методология синтеза гибридных нечетких решающих правил, хорошо зарекомендовавшая себя в процессе решения задач с нечётким описанием исследуемых классов со структурой данных аналогичной решаемой в работе задачи. В ходе проводимых исследований были синтезированы математические модели прогнозирования возникновения и развития тромботических прецедентов. Экспертное оценивание и математическое моделирование показали, что уверенность в правильном принятии решений по прогнозу появления и развития исследуемого класса тромботических осложнений превышает величину 0,9. В работе получены нечёткие математические модели прогнозирования возникновения и развития тромботических прецедентов у людей с подтверждённой коронавирусной инфекцией, для которой ведущим фактором риска является вторичный антифосфолипидный синдром с возникновением микроангиопатии. В ходе проведенных исследований была показана целесообразность использования полученных результатов в практике работы таких врачей, как иммунологи, инфекционисты, пульмонологи, кардиологи и сердечно-сосудистые хирурги The aim of the study is to develop a method for predicting the occurrence and development of thrombotic complications (thrombotic precedents) provoked by the action of a new coronavirus infection (COVID-19) on the human body, which allows improving therapeutic and diagnostic measures for patients with this pathology. The methodology of synthesis of hybrid fuzzy decision rules was chosen as the basic mathematical apparatus, which proved itself well in the process of solving problems with a fuzzy description of the classes under study with a data structure similar to the problem being solved in the work. In the course of the research, mathematical models for predicting the occurrence and development of thrombotic precedents were synthesized. Expert evaluation and mathematical modeling have shown that confidence in the correct decision-making on the prognosis of the occurrence and development of the studied class of thrombotic complications exceeds 0.9. The paper presents fuzzy mathematical models for predicting the occurrence and development of thrombotic precedents in people with confirmed coronavirus infection, for which the leading risk factor is secondary antiphospholipid syndrome with the occurrence of microangiopathy. In the course of the conducted studies, the expediency of using the results obtained in the practice of such doctors as immunologists, infectious disease specialists, pulmonologists, cardiologists and cardiovascular surgeons was shown


2008 ◽  
Vol 42 (2) ◽  
pp. 60-63
Author(s):  
N. A. Korenevskii ◽  
D. V. Khodeev ◽  
S. M. Yatsun

2011 ◽  
Vol 48-49 ◽  
pp. 357-361
Author(s):  
Bing Huang

By introducing a degree dominance relation to dominance interval intuitionistic fuzzy decision systems, we establish a degree dominance interval rough set model (RSM), which is mainly based on replacing the indiscernibility relation in classical rough set theory with the degree dominance interval relation. To simplify knowledge representation and extract some nontrivial simpler degree dominance interval intuitionistic fuzzy decision rules, we propose two attribute reductions of the degree dominance interval intuitionistic fuzzy decision systems that eliminate the redundant condition attributes that are not essential from the viewpoint of degree dominance interval intuitionistic fuzzy decision rules.


2009 ◽  
pp. 201-217
Author(s):  
Malcolm J. Beynon

This chapter considers the role of fuzzy decision trees as a tool for intelligent data analysis in domestic travel research. It demonstrates the readability and interpretability the findings from fuzzy decision tree analysis can pertain, first presented in a small problem allowing the fullest opportunity for the analysis to be followed. The investigation of the traffic fatalities in the states of the US offers an example of a more comprehensive fuzzy decision tree analysis. The graphical representations of the fuzzy based membership functions show how the necessary linguistic terms are defined. The final fuzzy decision trees, both tutorial and US traffic fatalities based, show the structured form the analysis offers, as well as more readable decision rules contained therein.


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