Classify data using naive bays using LingPipe

Question

I want to classify certain data into different classes based on its contents. I did this using the naive bike classifier, and I get the output as the best category to which it belongs. But now I want to classify the news, other than those that were in the training set, into the class “others”. I cannot manually add all the data, other than the training data, to a specific class, because it has a huge number of other categories. Is there a way to classify other data?

private static File TRAINING_DIR = new File("4news-train");
private static File TESTING_DIR = new File("4news-test");
private static String[] CATEGORIES = { "c1", "c2", "c3", "others" };

private static int NGRAM_SIZE = 6;

public static void main(String[] args) throws ClassNotFoundException, IOException {
    DynamicLMClassifier<NGramProcessLM> classifier = DynamicLMClassifier.createNGramProcess(CATEGORIES, NGRAM_SIZE);
    for (int i = 0; i < CATEGORIES.length; ++i) {
        File classDir = new File(TRAINING_DIR, CATEGORIES[i]);
        if (!classDir.isDirectory()) {
            String msg = "Could not find training directory=" + classDir + "\nTraining directory not found";
            System.out.println(msg); // in case exception gets lost in shell
            throw new IllegalArgumentException(msg);
        }

        String[] trainingFiles = classDir.list();
        for (int j = 0; j < trainingFiles.length; ++j) {
            File file = new File(classDir, trainingFiles[j]);
            String text = Files.readFromFile(file, "ISO-8859-1");
            System.out.println("Training on " + CATEGORIES[i] + "/" + trainingFiles[j]);
            Classification classification = new Classification(CATEGORIES[i]);
            Classified<CharSequence> classified = new Classified<CharSequence>(text, classification);
            classifier.handle(classified);
        }
    }
}

Answer 1

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Answer 2

"" ,

P(y|x) ~ P(y)P(x|y)

P(x) , x y. ,

cl(x) = "other" iff max_{over y}(P(y|x)) < T

T , ,

T = min_{over x and y in Training set}( P(y|x) )