📦 sorend / mecj

Multimodal Evolutionary Classifier, Java implementation ==============

This is a Java implementation of the Multi-modal Evolutionary Classifier.

The multi-modal evolutionary classifier is learns a reference vector for each of the classes in the training data by applying a genetic algorithm (GA) to the the following optimization problem:

Minimize: Sum(d(c, x)) for x in X

where c is a chromosome in the GA, and x is a training example from the set of training examples X, and d(a, b) is a distance measure. It is called multi-modal because the GA is able to capture minimization that an uni-modal optimization can not (e.g. gradient descent). Meaning, it can deal with data containing more local optima.

Once the reference vectors are learned, classification is done by selecting the reference vector nearest to the new data:

y = arg min d(x, c_i) for i in K

where y is the predicted class, d(a, b) is the distance measure, x is the new data, c_i is the reference vector for the i'th class in the set of classes K.

The distance measure d(a, b) can be substituted by any of the available distance measures. For example the stoean and minkowski distance measures are expected to work better for non-normalized data, while manhattan and euclidean are faster (expected to be the same) on normalized data.

Using in Eclipse

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To load the project in Eclipse, use the standard Gradle way:

• Run `gradlew eclipse`
• In Eclipse: File > Import > Import Existing Project into Workspace

You should now be up and running in Eclipse.

Using with WEKA

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Build and add the jar file to WEKAs class path. You can now find the MEC under the "rules" section in the classifiers.

Using in JAVA code

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You can use the code directly in JAVA without the WEKA wrapper. The fit method is used for training and expects input as a matrix of double (array of array of double) and an array of integers representing the classes. After this you can use the predict method to predict new examples. It expects the same input, matrix of double and will give array of integers back.

A more complete example below:

DistanceFactory df = MinkowskiDistanceFunction.Factory(1.5); MultimodalEvolutionaryClassifier mec = new MultimodalEvolutionaryClassifier(100, df);

// define some training data and associated classes double[][] X_train = new double[][]{ { 1, 2, 4 }, { 2, 4, 8 } }; int[] Y_train = new int[] { 0, 1 };

// train the classifier mec.fit(Xtrain, Ytrain);

// define some testing data double[][] X_test = new double[][]{ {0.9, 1.7, 4.5}, {2.1, 3.9, 7.8} };

// predict classes of the testing data int[] Ytest = mec.predict(Xtest);

System.out.println("Y_actual = " + ListUtil.prettyArray(new int[]{ 0, 1 })); System.out.println("Ytest = " + ListUtil.prettyArray(Ytest));

Credits

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The code has been implemented by Soren A. Davidsen as part of his research work at S. V. University, India. You can contact E. Sreedevi and K. Vijayalakshmi for information regarding the demonstration applications (part of their research work).