Big Data Classification Using Enhanced Dynamic KPCA and Convolutional Multi-Layer Bi-LSTM Network

ABSTRACT

Over the last few years, Big Data has sparked a lot of interest in a variety of engineering and scientific fields. Despite its numerous advantages, big data presents several issues such as big data management, big data analytics, big data security, and privacy which must be addressed to improve service quality. The proposed method focuses on data preprocessing, feature extraction, and classification. At first, data preprocessing has been performed and the weights have been assigned by the automated weight assignment model. In addition, the feature extraction model applies Principal Component Analysis (PCA) which employs feature correlation at the initial level, with best Information Gain (IG) and time-series data’s similarity search with Kernel Dynamic Time Wrapping (DTW) method. On this basis, an Enhanced dynamic KPCA algorithm is introduced by integrating the kernel trick, DTW algorithm, PCA, and information gain. Then Convolutional multi-layer Bi-LSTM algorithms have been applied for classification. To analyze the big data performance, 8 types of datasets such as 32-pendigits, Bank-Marketing, Click Prediction, EEG, Electricity-normalized, Jm1, Magic telescope, and Amazon employee access are used. The performances are evaluated by different metrics with existing methods of LSTM, BRNN, MFC, and DL methods.

KEYWORDS:

• Big data
• Convolutional multi-layer bi-LSTM
• Information gain
• Kernel dynamic time wrapping
• Multilayer self-attention

DISCLOSURE STATEMENT

No potential conflict of interest was reported by the author(s).

Additional information

Notes on contributors

Gnanendra Kotikam

Gnanendra Kotikam received his BTech degree in computer science and engineering from Jawaharlal Nehru Technological University, Hyderabad, India, in 2008 and MTech degree in computer science and engineering from the same University in 2011. He is currently pursuing PhD at the Department of Information and Communication Engineering, Anna University, Chennai, India. His areas of interest are big data, artificial intelligence, machine learning and deep learning.

S. Lokesh

S Lokesh received BE degree in computer science and engineering in 2005 from Anna University, ME degree in computer science and engineering from Anna University in 2007, and PhD in information and communication engineering in 2015 from Anna University, respectively. He is having 15 years of teaching experience and currently working as associate professor at Department of Computer Science and Engineering, PSG Institute of Technology and Applied Research, Coimbatore. His research areas are human–computer interaction, speech recognition, data analytics, and machine learning. E-mail: [email protected]