Simple Fuzzy Logic Rules Based on Fuzzy Decision Tree for Classification and Prediction Problem

Baldwin, JF; Xie, Dong

doi:10.1007/0-387-23152-8_23

Cited by 17 publications

(19 citation statements)

References 1 publication

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“…The most important AI techniques that have been used by researchers are Artificial Neural Network (ANN), Support Vector Machine, Fuzzy Logic systems, K-mean classifier, and many others [3,[7][8][9][10][11]. ANN is considered to be the most popular one among all.…”

Section: Dm and Ai Techniquesmentioning

confidence: 99%

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Decision Support System for Diabetes Mellitus through Machine Learning Techniques

Rashid¹,

Abdulla²,

Abdulla³

2016

ijacsa

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Abstract-recently, the diseases of diabetes mellitus have grown into extremely feared problems that can have damaging effects on the health condition of their sufferers globally. In this regard, several machine learning models have been used to predict and classify diabetes types. Nevertheless, most of these models attempted to solve two problems; categorizing patients in terms of diabetic types and forecasting blood surge rate of patients. This paper presents an automatic decision support system for diabetes mellitus through machine learning techniques by taking into account the above problems, plus, reflecting the skills of medical specialists who believe that there is a great relationship between patient's symptoms with some chronic diseases and the blood sugar rate. Data sets are collected from Layla Qasim Clinical Center in Kurdistan Region, then, the data is cleaned and proposed using feature selection techniques such as Sequential Forward Selection and the Correlation Coefficient, finally, the refined data is fed into machine learning models for prediction, classification, and description purposes. This system enables physicians and doctors to provide diabetes mellitus (DM) patients good health treatments and recommendations.

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Section: Dm and Ai Techniquesmentioning

confidence: 99%

“…Fuzzy logic classifier model is another type of AI tools that has been utilized by researchers [11] to categorize cases into type-1 and type-2. Their work relied on a secondary type of www.ijacsa.thesai.org database called Pima dataset.…”

Section: Dm and Ai Techniquesmentioning

confidence: 99%

Decision Support System for Diabetes Mellitus through Machine Learning Techniques

Rashid¹,

Abdulla²,

Abdulla³

2016

ijacsa

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“…Introduced by Breiman et al (1984), decision trees are one of the few knowledge representation schemes, which are easily interpreted and may be inferred by very simple learning algorithms (Baldwin and Xie, 2005). A decision tree is a tree in which:…”

Section: Decision Treesmentioning

confidence: 99%

Evaluation of biometric systems: a study of users' acceptance and satisfaction

El-Abed¹,

Giot²,

Hemery³

et al. 2012

IJBM

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This paper presents a modality-independent evaluation methodology to study users' acceptance and satisfaction of biometric systems. It uses a survey questionnaire for data collection, and some data-mining tools for their analysis. We have applied it on two biometric systems developed in our research laboratory. The results from this survey show the necessity of taking users' point of view when designing and evaluating biometric systems. A panel of 100 volunteers was more satisfied from the keystroke system than the face one. Users surprisingly considered that its perceived performance was also better, even if the used face system has a better performance with an EER of 8.76% than the keystroke one with an EER of 17.51%. The robustness of a system against attacks, computation time required during the verification phase and its easiness to use have been identified as important factors influencing their opinions regarding the tested systems.

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“…These fuzzy models overcome the sharp boundary problem, 11,12 providing a soft controller surface and good accuracy in dealing with continuous attributes and prediction problem. We therefore developed these fuzzy models based on the mass assignments theory 2,6 such as fuzzy decision tree, 13 simple fuzzy logic rule, 14 fuzzy association rule, 15 fuzzy Bayesian net, 16 and prototype.…”

Section: Fuzzy Logic Data Mining and Machine Learning Tool Kitmentioning

confidence: 99%

Intelligent Fril/SQL interrogator

Xie

Baldwin

2007

Int. J. Intell. Syst.

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The intelligent Fril/SQL interrogator is an object-oriented and knowledge-based support query system, which is implemented by the set of logic objects linking one another. These logic objects integrate SQL query, support logic programming language-Fril and Fril query together by processing them in sequence in slots of each logic object. This approach therefore takes advantage of both object-oriented system and a logic programming-based system. Fuzzy logic data mining and a machine learning tool kit built in the intelligent interrogator can automatically provide a knowledge base or rules to assist a human to analyze huge data sets or create intelligent controllers. Alternatively, users can write or edit the knowledge base or rules according to their requirements, so that the intelligent interrogator is also a support logic programming environment where users can write and run various Fril programs through these logic objects.

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Simple Fuzzy Logic Rules Based on Fuzzy Decision Tree for Classification and Prediction Problem

Cited by 17 publications

References 1 publication

Decision Support System for Diabetes Mellitus through Machine Learning Techniques

Decision Support System for Diabetes Mellitus through Machine Learning Techniques

Evaluation of biometric systems: a study of users' acceptance and satisfaction

Intelligent Fril/SQL interrogator

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