Exploration of usability of PLSR for implementation in the RENT feature selection method

RENT (Repeated Elastic Net Technique) is a feature selection technique developed for binary classification and regression tasks. But most real life cases are multi-class. RENT is not currently capable of handling multi-class classification or regression problems. Our thesis is an attempt to extend RENT to handle multi-class problems. To this end we have explored the PLSR algorithm to study if it is a good option for multi-class classification tasks. We call this method PLSR-RENT. PLSR-RENT gives us a reduced set of features which are then used with different classifiers. The results obtained are compared with other feature selection algorithms. We observe that performance of PLSR-RENT is comparable to other feature selectors by very slight differences, though it is not better than others. More tests need to be conducted to conclude if PLSR-RENT is the best option for extending RENT, but it is a good candidate

Data Analysis for Physical Activity Monitoring

Master's thesis in Computer SciencePhysical activity is essential for humans for maintaining a healthy and comfortable lifestyle. With science and technological advancements, there comes various guidelines for the amount of physical activity a person should perform. Monitoring the physical activity enables us to follow those guidelines and be aware of own activity. Wearable computing is allowing us to track and monitor our own performed physical activities by mostly intrinsic (minimal) interaction. Physical activity monitoring is an emerging research area in wearable computing. Our thesis is about identifying and classifying which activity is being performed. We have used various classifiers and evaluation metrics to validate our classifier models