FURIA Classification using WEKA Java Code

FURIA is short form for Fuzzy unordered rule induction algorithm. It is the improved algorithm of existing RIPPER Algorithm. It has simple and comprehensive rule sets. Furthermore, it has many extensions with modifications. Instead of Conventional Rules & unordered rule sets, FURIA learns fuzzy rules only. This FURIA algorithm has an efficient rule stretching method for deal with uncovered examples. Compared with existing RIPPER, C4.5 and other classifiers, FURIA provides best classification results. In other wors, Classification Accuracy is Very High compared with others. 

Requirements:

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2 Jar Files

 --> weka-3.7.1-beta.jar

 --> weka-3.7.3.jar

2 Datasets

 --> ClevelandHeartDiseaseTrainingDataset.arff

 --> ClevelandHeartDiseaseTestingDataset.arff

How to Implement:

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ClevelandHeartDiseaseTrainingDataset.arff contains lot of patients Health Records. It has 5 attributes and 1 class attribute.

  1) sex: patient sex (1 = male, 0 = female), 

  2) cp: chest pain type (1 = typical angina, 2 = atypical angina, 3 = non-anginal pain, 4 = asymptomatic), 

  3) slope: the slope of the peak exercise ST segment (1 = upsloping, 2 = flat, 3 = downsloping)

  4) ca: number of major vessels (0-3) colored by flourosopy

  5) thal: (3 = normal, 6 = fixed defect, 7 = reversable defect)

  6) class: (0 = no heart disease, 1 = presence of heart disease)

  

This Classification Algorithm classify this training dataset. After Classification, it generate some classification rules. Followed by, this algorithm load ClevelandHeartDiseaseTestingDataset.arff. This testing dataset contains 5 attributes with one class attribute. This class attribute contains (?) question mark. Because we predict each testing record is possible to presence of heart disease or not. Then this classification algorithm predicts each records class attribute value based on classification rules (It is generated after Training Process).

How to Run this Code in Command Prompt:

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>set classpath=%classpath%;weka-3.7.1-beta.jar;

>set classpath=%classpath%;weka-3.7.3.jar;

>javac FURIAClassification.java

>java FURIAClassification

Output: FURIAOutput.txt