SVM Classification using WEKA Java Code

In machine learning, support vector machines (SVMs, also support vector networks) are supervised learning models with associated learning algorithms that analyze data used for classification and regression analysis. A Support Vector Machine (SVM) is a discriminative classifier formally defined by a separating hyperplane. In other words, given labeled training data (supervised learning), the algorithm outputs an optimal hyperplane which categorizes new examples. The vectors (cases) that define the hyperplane are the support vectors. SVM uses a technique called the kernel trick to transform your data and then based on these transformations it finds an optimal boundary between the possible outputs.

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 SVMClassification.java

>java SVMClassification

Output: SVMOutput.txt