openXDATA: A Tool for Multi-Target Data Generation and Missing Label Completion

TL;DR

The paper introduces openXDATA, a tool that completes missing labels in datasets with disjoint label spaces using a multi-task neural network, enabling improved multi-target data generation and label estimation.

Contribution

It presents the cross-data label completion (CDLC) algorithm and demonstrates its effectiveness across multiple emotion datasets with different label types.

Findings

Successfully estimated both categorical and continuous labels.

Achieved label estimation rates approaching ground truth.

Demonstrated effectiveness across diverse emotion datasets.

Abstract

A common problem in machine learning is to deal with datasets with disjoint label spaces and missing labels. In this work, we introduce the openXDATA tool that completes the missing labels in partially labelled or unlabelled datasets in order to generate multi-target data with labels in the joint label space of the datasets. To this end, we designed and implemented the cross-data label completion (CDLC) algorithm that uses a multi-task shared-hidden-layer DNN to iteratively complete the sparse label matrix of the instances from the different datasets. We apply the new tool to estimate labels across four emotion datasets: one labeled with discrete emotion categories (e.g., happy, sad, angry), one labeled with continuous values along arousal and valence dimensions, one with both kinds of labels, and one unlabeled. Testing with drop-out of true labels, we show the ability to estimate both categories and continuous labels for all of the datasets, at rates that approached the ground truth values. openXDATA is available under the GNU General Public License from https://github.com/fweninger/openXDATA.