Deep Learning and Fuzzy Rule-Based Hybrid Fusion Model for Data Classification
Burra Lakshmi Ramani1, Padmaja Poosapati2, Praveen Tumuluru3, CH. M. H. Saibaba4, Mothukuri Radha5, K. Prasuna6
1Burra Lakshmi Ramani, Assistant Professor, Department of Computer Science and Engineering at PVP Siddhartha Institute of Technology (PVPSIT),Kanuru, Vijayawada (Andhra Pradesh), India.
2Dr. P. Padmaja, Professor, Department of Information Technology at Anil Neerukonda Institute of Technology and Science (ANITS), Visakhapatnam, (Andhra Pradesh), India.
3Praveen Tumuluru, Assistant Professor, Department of Computer Science and Engineering, Koneru Lakshmaiah Educational Foundation, Guntur, (Andhra Pradesh), India
4CH. M. H. Saibaba, Assistant Professor, Department of Computer Science and Engineering, Koneru Lakshmaiah Educational Foundation, Guntur, (Andhra Pradesh,) India.
5Mothukuri Radha, Assistant Professor, Department of Computer Science and Engineering, Koneru Lakshmaiah Educational Foundation, Guntur, (Andhra Pradesh), India.
6K. Prasuna, Assistant Professor, Department of Computer Science and Engineering, Koneru Lakshmaiah Educational Foundation, Guntur, (Andhra Pradesh), India.
Manuscript received on 08 March 2019 | Revised Manuscript received on 16 March 2019 | Manuscript published on 30 July 2019 | PP: 3205-3213 | Volume-8 Issue-2, July 2019 | Retrieval Number: B2304078219/19©BEIESP | DOI: 10.35940/ijrte.B2304.078219
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Data mining is the promising field that attracted the industries to manage huge volumes of data. The most effective and challenging techniques of data mining is data classification. The main intension of this research is to design and develop a data classification strategy based on hybrid fusion model using the deep learning approach, Adaptive Lion Fuzzy System (ALFS), and Robust Grey wolf based Sine Cosine Algorithm based Fuzzy System (RGSCA-FS). The hybrid model consists of three phases: In the first phase, the data is classified using ALFS and the rule base of the fuzzy system is updated by optimally generating the rules using adaptive lion optimization (ALA) from the training data. The second step is the fuzzification process, which converts the scalar values in the training data into fuzzy values with the help of membership function, which is based on Adaptive Genetic Fuzzy System (AGFS). Finally, the classified score of data instances is determined using defuzzification process, which converts the linguistic variable into fuzzy score. In the second phase, the data is classified using Robust Grey wolf based Sine Cosine Algorithm based Fuzzy System (RGSCA-FS), which is used for selecting the optimal fuzzy rules. In the third phase, the data is classified using deep learning networks. The outputs from three phases are fused together using the hybrid fusion model for which the weighed fusion is employed. The performance of the system is validated using three different datasets that are available in UCI machine learning repository. The proposed hybrid model outperforms the existing methods with sensitivity of 0.99, specificity of 0.9350, and accuracy of 0.9411, respectively.
Keywords: Data Classification, Data Mining, Defuzzification, Fuzzy Rules, Membership Function.
Scope of the Article: Data Mining