Similarity Measure of Breast Cancer Datasets Using Fuzzy Rule-Based Classification by Attribute

2019 ◽  
Vol 1 (1) ◽  
pp. 12-28
Author(s):  
Tengyue Li ◽  
Simon Fong
Keyword(s):  
Breast Cancer ◽  
Information Gain ◽  
Fuzzy Rule ◽  
Ensemble Methods ◽  
Classification Algorithms ◽  
Rule Generation ◽  
Rule Based ◽  
Reasoning System ◽  
Evaluation Approach ◽  
Test Instance

To compare with two datasets based on attributes by using classification algorithms, for the attributes, the authors need to select them by rules and the system is known as rule-based reasoning system which classifies a given test instance into a particular outcome from the learned rules. The test instance carries multiple attributes, which are usually the values of diagnostic tests. In this article, the authors propose a classifier ensemble-based method for comparison of two breast cancer datasets. The ensemble data mining learning methods are applied to rule generation, and a multi-criterion evaluation approach is used for selecting reliable rules over the results of the ensemble methods. The efficacy of the proposed methodology is illustrated via an example of two breast cancer datasets. This article introduces a novel fuzzy rule-based classification method called FURIA, to obtain a relationship between two breast cancer datasets. Hence, it can find the similarity between these two datasets. The new method is compared vis-à-vis with other classical statistical approaches such as correlation and mutual information gain.

MEASURING SIMILARITY BETWEEN BIOMEDICAL DATA BY USING FURIA ENSEMBLES RULE-BASED CLASSIFICATION

2020 ◽  
Vol 2 (1) ◽  
pp. 116-127
Author(s):  
Simon Fong
Keyword(s):  
Similarity Measures ◽  
Fuzzy Rule ◽  
Ensemble Methods ◽  
Biomedical Data ◽  
Rule Based ◽  
Reasoning System ◽  
Evaluation Approach ◽  
Common Task ◽  
Test Instance ◽  
The Difference

Similarity measures are essential to solve many pattern recognition problems such as classification, clustering, and retrieval problems. Various distance/similarity measures that is applicable to compare two probability density functions. Data comparison is widely used field in our society nowadays, and it is a very import part. To compare two objects is a common task that people from all walks of life would do. People always want or need to find the similarity between two different objects or the difference between two similar objects. Some different data may share some similarity in some given attribute(s). To compare with two datasets based on attributes by classification algorithms, for the attributes, we need to select them out by rules and the system is known as rule-based reasoning system or expert system which classifies a given test instance into a particular outcome from the learned rules. The test instance carries multiple attributes, which are usually the values of diagnostic tests. In this article, we are proposing a classifier ensemble-based method for comparison of two datasets or one dataset with different features. The ensemble data mining learning methods are applied for rule generation, and a multi-criterion evaluation approach is used for selecting reliable rules over the results of the ensemble methods. The efficacy of the proposed methodology is illustrated via an example of two disease datasets; it is a combined dataset with the same instances and normal attributes but the class in strictly speaking. This article introduces a fuzzy rule-based classification method called FURIA, to get the relationship between two datasets by FURIA rules. And find the similarity between these two datasets.

Constructing Cost-Sensitive Fuzzy-Rule-Based Systems for Pattern Classification Problems

2007 ◽  
Vol 11 (6) ◽  
pp. 546-553 ◽  
Author(s):  
Tomoharu Nakashima ◽  
◽  
Yasuyuki Yokota ◽  
Hisao Ishibuchi ◽  
Gerald Schaefer ◽  
...  
Keyword(s):  
Pattern Classification ◽  
A Priori ◽  
Fuzzy Rule ◽  
Fuzzy Classification ◽  
Classification Error ◽  
Rule Generation ◽  
Classification Problems ◽  
Misclassification Cost ◽  
Rule Based ◽  
Rule Based Systems

We evaluate the performance of cost-sensitive fuzzy-rule-based systems for pattern classification problems. We assume that a misclassification cost is given a priori for each training pattern. The task of classification thus becomes to minimize both classification error and misclassification cost. We examine the performance of two types of fuzzy classification based on fuzzy if-then rules generated from training patterns. The difference is whether or not they consider misclassification costs in rule generation. In our computational experiments, we use several specifications of misclassification cost to evaluate the performance of the two classifiers. Experimental results show that both classification error and misclassification cost are reduced by considering the misclassification cost in fuzzy rule generation.

scholarly journals Examination of Healthcare Diagonosis using Iot

2019 ◽  
Vol 9 (1) ◽  
pp. 5251-5254
Keyword(s):  
Cloud Computing ◽  
Fuzzy Rule ◽  
Classification Algorithms ◽  
Computing Technology ◽  
Rule Based ◽  
Related Data ◽  
Health Related ◽  
Neural Classifier ◽  
Iot Devices ◽  
Existing Data

In recent years, online applications, spare many services for wellness of health related issues. The application is kept updated so that the health related data are kept modernized for future references. The application collects information from IoT devices and then compares them with other existing data from the prevailing records with the same disease. The collected data is then reserved in a database that hold all records about the healthcare issues. Cloud computing technology is used to guard and reserve the healthcare records. Cloud and IoT technology are connected to provide users with a completely developed healthcare record. The existing system makes use of Fuzzy Rule Based Neural Classifier that helps in assembling and categorizing the diabetes data under the guidance of severity analyzer. This work, present the comparison of some classification algorithms and obtain the accuracy, the dataset collected is a real-time dataset. The output and results are tabulated after the comparison of the algorithms.

scholarly journals Performance Comparison of Rule Based Classification Algorithms

2011 ◽  
pp. 137-142
Author(s):  
Prafulla Gupta ◽  
Durga Toshniwal
Keyword(s):  
Performance Comparison ◽  
Classification Algorithms ◽  
Rule Generation ◽  
Associative Classification ◽  
Rule Mining ◽  
Rule Based ◽  
First Order ◽  
Rule Analysis ◽  
Basic Ideas ◽  
Predictive Rule

Classification based on predictive association rules (CPAR) is a kind of association classification methods which combines the advantages of both associative classification and traditional rule-based classification. For rule generation, CPAR is more efficient than traditional rule-based classification because much repeated calculation is avoided and multiple literals can be selected to generate multiple rules simultaneously. CPAR inherits the basic ideas of FOIL (First Order Inductive Learner) algorithm and PRM (Predictive Rule Mining) algorithm in rule generation. It integrates the features of associative classification in predictive rule analysis. In comparison of FOIL, PRM algorithm usually generates more rules. PRM uses concept of lowering weights rather than removing tuple if tuple is satisfied by the rule. The distinction between CPAR and PRM is that instead of choosing only the attribute that displays the best gain on each iteration CPAR may choose a number of attributes if those attributes have gain close to best gain.

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