Java调用多标签学习开源库Mulan

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Java调用多标签学习开源库Mulan

环境准备

下载Mulan并导入Jar包。Mulan下载地址：http://mulan.sourceforge.net/download.html
下载多标签数据集。下载地址：http://mulan.sourceforge.net/datasets-mlc.html

源码

代码为mulan提供的ML-KNN算法的实验代码，本源码原作者为：Eleftherios Spyromitros-Xioufis (espyromi@csd.auth.gr)，稍做了改动。

import mulan.data.MultiLabelInstances;import mulan.evaluation.Evaluator;import mulan.evaluation.MultipleEvaluation;import mulan.evaluation.measure.*;import java.util.ArrayList;import java.util.List;import java.util.logging.Level;import java.util.logging.Logger;public class MLkNN {    public static void main(String[] args) {        try {            String path = "../dataset/bookmarks/";            System.out.println("Loading the data set");            MultiLabelInstances dataSet = new MultiLabelInstances(path + "bookmarks.arff", path + "bookmarks.xml");            Evaluator eval = new Evaluator();            MultipleEvaluation results;            List<Measure> measures = new ArrayList<>(5);            measures.add(new HammingLoss());            measures.add(new OneError());            measures.add(new Coverage());            measures.add(new RankingLoss());            measures.add(new AveragePrecision());            int numOfNeighbors;            for (int i = 8; i <= 12; i++) {                System.out.println("MLkNN Experiment for " + i + " neighbors:");                numOfNeighbors = i;                double smooth = 1.0;                mulan.classifier.lazy.MLkNN mlknn = new mulan.classifier.lazy.MLkNN(numOfNeighbors, smooth);                // mlknn.setDebug(true);                results = eval.crossValidate(mlknn, dataSet, measures, 10);                System.out.println(results);            }        } catch (Exception ex) {            Logger.getLogger(MLkNN.class.getName()).log(Level.SEVERE, null, ex);        }    }}

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