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.