FingerprintHarGasFacialDataset

This dataset has 116 rows, each one representing one dataset from a set of several public datasets, taken from several different domains (Human Activity Recognition, Gas analysis, Facial expressions recognition using Kinect), so it is actually a meta-data set (a dataset of datasets) as well as different sources:

- University of Texas Multimodal Human Action Dataset
- Opportunity Activity Recognition
- Physical Activity Monitoring for Aging People Version 2
- Mobile Health data set
- Daily and Sports Activities
- Human Activities and Postural Transitions
- Gas Sensor Array Drift
- Grammatical Facial Expressions

Each of the columns is a feature taken from one of the above mentioned 3 domains:
- The first 9 (F0-F8) are taken from the HAR domain
- The next 3 (F9-F11) come from the Gas domain
- The last 3 (F12-F14) come from the Face expressions domain
Notice that there are more rows than public datasets we compiled because for some of them we took pairs of sensors as a different dataset.

The label (last column) is the fusion architecture that performed best, from the following ones:

1.- Aggregation of features
2.- Vote with shuffled features
3.- Voting
4.- Voting with CART, LR, and RCF for all features
5.- Multi-View Stacking with shuffled features
6.- Multi-View Stacking
7.- Multi-View Stacking with CART, RCF, and LR
8.-AdaBoost

When we found no statistically significant difference between the default method, which was aggregation, then we took this one.