Java 木兰分类数据-不起作用

我想用木兰对一些数据进行分类。但我有一个例外：

mulan.data.DataLoadException: Error creating Instances data from supplied Reader data source
at mulan.data.MultiLabelInstances.loadInstances(MultiLabelInstances.java:469)
at mulan.data.MultiLabelInstances.loadInstances(MultiLabelInstances.java:458)
at mulan.data.MultiLabelInstances.<init>(MultiLabelInstances.java:168)

至于数据格式，我有一个称为title的维度，有160个分类

数据文件按照arff格式进行格式化

有些文本是中文的

任何帮助都将不胜感激

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致以最诚挚的问候

这看起来像是木兰中的一只虫子

有关该错误的更多详细信息

public class TrainTestExperiment {

    public static void main(String[] args) {
        try {
            String path = Utils.getOption("path", args); // e.g. -path dataset/
            String filestem = Utils.getOption("filestem", args); // e.g. -filestem emotions
            String percentage = Utils.getOption("percentage", args); // e.g. -percentage 50 (for 50%)

            System.out.println("Loading the dataset");
            MultiLabelInstances mlDataSet = new MultiLabelInstances(path + filestem + ".arff", path + filestem + ".xml");

            // split the data set into train and test
            Instances dataSet = mlDataSet.getDataSet();
            RemovePercentage rmvp = new RemovePercentage();
            rmvp.setInvertSelection(true);
            rmvp.setPercentage(Double.parseDouble(percentage));
            rmvp.setInputFormat(dataSet);
            Instances trainDataSet = Filter.useFilter(dataSet, rmvp);

            rmvp = new RemovePercentage();
            rmvp.setPercentage(Double.parseDouble(percentage));
            rmvp.setInputFormat(dataSet);
            Instances testDataSet = Filter.useFilter(dataSet, rmvp);

            MultiLabelInstances train = new MultiLabelInstances(trainDataSet, path + filestem + ".xml");
            MultiLabelInstances test = new MultiLabelInstances(testDataSet, path + filestem + ".xml");

            Evaluator eval = new Evaluator();
            Evaluation results;

            Classifier brClassifier = new NaiveBayes();
            BinaryRelevance br = new BinaryRelevance(brClassifier);
            br.setDebug(true);
            br.build(train);
            results = eval.evaluate(br, test);
            System.out.println(results);
        } catch (Exception e) {
            e.printStackTrace();
        }
    }
}