Towards the effectiveness of Deep Convolutional Neural Network based Fast Random Forest Classifier

Deep Learning is considered to be a quite young in the area of machine learning research, found its effectiveness in dealing complex yet high dimensional dataset that includes but limited to: images, text and speech etc. with multiple levels of representation and abstraction. As there are plethora of research on these datasets by various researchers , a win over them needs a lots of attention. Careful setting of Deep learning parameters are of paramount importance in order to avoid the overfitting unlike conventional methods with limited parameter settings. Deep Convolutional neural network (DCNN) with multiple layer of compositions and appropriate settings might be is an efficient machine learning method that can outperform the conventional methods in a great way. However, due to its slow adoption in learning, there are also always a chance of overfitting during feature selection process, which can be addressed by employing a regularization method called “dropout”. Fast Random Forest (FRF) is a powerful ensemble classifier especially when the datasets are noisy and when the number of attributes are large in comparison to the number of instances, as is the case of Bioinformatics datasets. Several publicly available Bioinformatics dataset, Handwritten digits recognition and Image segmentation dataset are considered for evaluation of the proposed approach. The excellent performance obtained by the proposed DCNN based feature selection with FRF classifier on high dimensional datasets makes it a fast and accurate classifier in comparison the state-of-the-art. Key word: Deep Learning, Convolutional neural Network, dropout, Fast Random Forest, Bioinformatics, Handwritten digits, segmentation, Classification, t-test