Weka jar java
Converting dataset composed of both numerical and nominal attributes and a nominal target (i.e. Although the detailed Java Doc can be found here, I briefly discuss a number of examples to help you use "matlab2weka.jar" in the MATLAB environment.įirst, in order to use this code, you must define the path for the JAR file to the MATLAB and impart the matlab2weka package using the following code. The matlab2weka.jar can handle both nominal and numerical attribute values as well as nominal and numerical class values.
#Weka jar java code
Thus, I implemented the same code in Java and it runs much faster. However, I found this code extremely slow because it uses extensive amount of loops. This code was originally motivated by the work of Matt Dunham where he used a MATLAB file to convert the MATLAB dataset to an Instances object of Weka. This is a Java code that converts the MATLAB dataset into an Instances object of Weka. This is an example that performs unsupervised clustering on the IRIS dataset using a clustering algorithm available in Weka. This is an example that performs regression on the Imported-Car dataset using a regression algorithm available in Weka. This is an example that performs cost sensitive classification on the IRIS dataset using a classifier available in Weka.
This is an example that performs multi-class classification on the IRIS dataset using a classifier available in Weka. You can increase (or decrease) the heap size at File -> Preference -> General -> Java Heap Memory. If you ever receive an error message that is similar to ": Java heap space," then you need to manually increase the heap size. Some of the functions in Weka (e.g., Gaussian Process Regression) require a large Java heap size within the MATLAB environment. : There was a small bug in wekaRegression.m and regression_example.m, which is now fixed. 2) The classifier & cost-sensitive classifier now produces "nominal outputs" rather than "numerical outputs". : 1) The input files for example codes have been added since some older versions of MATLAB don't have them built in. 2) The "crossvalind" function, which requires the Bioinformatics toolbox, is replaced with idxCV = ceil(rand()*K)+1.
#Weka jar java mac
: 1) Paths to WEKA has been updated to comply with Mac users. You may also find the same information at the Matlab Central. If you would like to collaborate to improve the code or if you find any bugs, please don't hesitate to reach me at " silee org". This work is still in-progress and I have only included codes that I mainly use for my work. Here I introduce an efficient MATLAB to Weka interface, which was implemented based on the initial work of Matt Dunham. Fortunately, Weka was implemented in Java, and MATLAB had a wrapper that allows communicating with Java. I do most of my analyses on MATLAB, so I was searching for an interface between MATLAB and Weka. For instance, I often needed to perform the analysis based on leave-one-out- subject cross-validation, but it was quite difficult to do this on Weka GUI. Although Weka provides fantastic graphical user interfaces (GUI), sometimes I wished I had more flexibility in programming Weka. Weka is an open-source platform providing various machine learning algorithms for data mining tasks. College of Information and Computer Science