Data augmentation In Machine Learning

Data augmentation is a common method in deep learning used to reduce the effect of overfitting. The idea is to expand an existing data set using only the available data so that the learning algorithm can more effectively extract those features essential to the task. To train deep learning models, typically big data sets are required, usually from manual data collection or from already existing databases. However, in some cases only a limited data set is available, Therefore, to expand the size of the data set, data augmentation can be employed, The complex indoor environment and APs may cause problems because of the limited coverage of Wi-Fi APs   RSSI measurements. The purpose of data augmentation, in this case, is to detect and remove faulty measurement data or to remove invalid data, thus improving the accuracy and efficiency of the entire positioning system by creating a database representation that is more suitable for downstream deep learning classifiers. Data augmentation adds value to base data by adding information derived from internal and external sources within the database. It can also reduce the manual intervention required to develop meaningful information and gain insight from business data, as well as significantly enhancing data quality. In this way, we can produce multiple copies of available data with slight variations. Some common techniques used in data augmentation include extrapolation, in which the relevant fields are updated or assigned valued based on heuristics; tagging, in which common recorded are tagged to a group, making it easier for the group to be understood and differentiated, aggregation in which values are estimated for relevant fields if needed using mathematical averages and means and probability, in which values are populated based on the probability of events based on heuristics and analytical statistics.