Protein Fold recognition (PFR) is considered as an important step towards protein structure prediction. It also provides significant information about general functionality of a given protein. Despite all the efforts have been made, PFR still remains unsolved. It is shown that appropriately extracted features from the physicochemical-based attributes of the amino acids plays crucial role to address this problem. In this study, we explore 55 different physicochemical-based attributes using two novel feature extraction methods namely segmented distribution and segmented density. Then, by proposing an ensemble of different classifiers based on the AdaBoost.M1 and Support Vector Machine (SVM) classifiers which are diversely trained on different combinations of features extracted from these attributes, we outperform similar studies found in the literature for over 2% for the PFR task.