Orange Blog
By: BIOLAB, Jan 9, 2012
Multi-label classification (and Multi-target prediction) in Orange
The last summer, student Wencan Luo participated in Google Summer of Code to implement Multi-label Classification in Orange. He provided a framework, implemented a few algorithms and some prototype widgets. His work has been “hidden” in our repositories for too long; finally, we have merged part of his code into Orange (widgets are not there yet …) and added a more general support for multi-target prediction. You can load multi-label tab-delimited data (e.
By: BIOLAB, Sep 2, 2011
GSoC Review: Multi-label Classification Implementation
Traditional single-label classification is concerned with learning from a set of examples that are associated with a single label l from a set of disjoint labels L, |L| > 1. If |L| = 2, then the learning problem is called a binary classification problem, while if |L| > 2, then it is called a multi-class classification problem (Tsoumakas & Katakis, 2007). Multi-label classification methods are increasingly used by many applications, such as textual data classification, protein function classification, music categorization and semantic scene classification.
By: BIOLAB, Jul 20, 2011
Orange GSoC: Multi-label Classification Implementation
Multi-label classification is one of the three projects of Google Summer Code 2011 for Orange. The main goal is to extend the Orange to support multi-label, including dataset support, two basic multi-label classifications-problem-transformation methods & algorithm adaptation methods, evaluation measures, GUI support, documentation, testing, and so on. My name is Wencan Luo, from China. I’m very happy to work with my mentor Matija. Until now, we have finished a framework for multi-label support for Orange.