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Multi-objective optimization in Machine & Deep learning : A Survey

Multi-objective optimization in ML & DL


Title :Multi-objective optimization in Machine & Deep learning : A Survey

Note: i) Use only last 3 years (2016-2019) in section 4 and 5.
ii) Use IEEE format only for references and citations.
iii) Include table in the section 5 along with text. Sample table is described.
iv) Include citations of only IEEE, Elseiver ,Science Direct, Springer and SIAM Journals.
v) Include mathematical aspects in text whereever possible

Outline of paper:
1) Abstract
2) Introduction
3) Background of Multi-objective optimization
3.1 Multi objective optimization –Definition, pareto set, pareto front , dominance rule with diagram ,mathematical and theoretical aspects.
3.3 Category/classification of multi objective optimization
3.4 Advantages and limitations of Multi-objective Optimization Techniques.

4) Multi objective optimization for Machine Learning and Deep Learning
4.1 Parameter optimization
4.2 Feature extraction
4.3 Model enhancement
– Classification
– Clustering
– Association rules
4.4 Robust Multi-objective optimized Machine Learning and Deep Learning.
4.5 Model selection, transfer learning
5) Real world Application of multi-objective optimization with machine learning and deep learning in the area of
5.1 Image analysis-medical images ,hyperspectral images ,microscopic images
5.2 Agriculture
5.3 Signal processing and fault diagnosis
5.4 Time Series Analysis
5.5 Curvature analysis
6)Conclusion and future work