A new Deep learning model for Sequential Pattern Mining using Ensemble learning and Models selection Taking Mobile Activity Recognition as a case

Abstract

Sequential pattern mining has gained popularity in Data Mining and pattern recognition. Most sequential pattern mining algorithms are influenced by noisy variables, parameters tuning, bias-variance dilemma and learning instability. This paper presents a new deep learning model for sequential pattern mining, by using ensemble learning and models selection. Experimental studies on mobile activity recognition showed that our deep learning model, which is named Deep Sequential Pattern Mining (abbreviated as DeepSPM), obtained an enhanced generalization in comparison with Long Short Term Memory (LSTM), Bidirectional Associative Memory (BAM) and Hopfield. We provide a comparative performance analysis of pattern recognition. The advantages and the drawbacks of the benchmarking models are critically discussed.