LSTM-ANN & BiLSTM-ANN: Hybrid deep learning models for enhanced classification accuracy

摘要

Machine learning is getting more and more advanced with the progression of state-of-the-art technologies. Since existing algorithms do not provide a palatable learning performance most often, it is necessary to carry on the trail of upgrading the current algorithms incessantly. The hybridization of two or more algorithms can potentially increase the performance of the blueprinted model. Although LSTM and BiLSTM are two excellent far and widely used algorithms in natural language processing, there still could be room for improvement in terms of accuracy via the hybridization method. Thus, the advantages of both RNN and ANN algorithms can be obtained simultaneously. This paper has illustrated the deep integration of BiLSTM-ANN (Fully Connected Neural Network) and LSTM-ANN and manifested how these integration methods are performing better than single BiLSTM, LSTM and ANN models. Undertaking Bangla content classification is challenging because of its equivocalness, intricacy, diversity, and shortage of relevant data, therefore, we have executed the whole integrated models on the Bangla content classification dataset from newspaper articles. The proposed hybrid BiLSTM-ANN model beats all the implemented models with the most noteworthy accuracy score of 93% for both validation & testing. Moreover, we have analyzed and compared the performance of the models based on the most relevant parameters.