Classifying multilabeled data points using Mulan

So far, we have seen multiclass classification that aims to classify a data instance into one of several classes. Multilabeled data instances are data instances that can have multiple classes or labels. The machine learning tools that we have used so far are not capable of handling data points that have this characteristic of having multiple target classes.

For classifying multilabeled data points, we will be using an open source Java library named Mulan. Mulan has implementations of various classification, ranking, feature selection, and evaluation of models. As Mulan does not have GUI, the only way to use it is either by command line or using its API. In this recipe, we will limit our focus on classification and evaluation of classification of a multilabeled dataset using two different classifiers.

Getting ready

In order to perform this recipe, we will require the following:

1. First, download Mulan. In our recipe, we will be using its version...