A dynamic framework for tuning SVM hyper parameters based on Moth-Flame Optimization and knowledge-based-search

Dhruba Jyoti Kalita

doi:10.1016/j.eswa.2020.114139

2021-04-01

PRE

Dhruba Jyoti Kalita *

Vibhav Prakash Singh

Vinay Kumar

DOI：10.1016/j.eswa.2020.114139

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摘要

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In the real world, most of the collections of data are dynamic in nature, i.e. their size may grow with time. This dynamic nature of the data not only reduces the performance of the classifiers but also demands more optimized models for retaining the performance. Due to this, machine learning models developed in a static environment cannot be deployed efficiently to solve the real-world problems. Nowadays, maximum existing works consider only the static behaviour of the data for the training of machine learning models where the size of the collection of training data does not change over time. This paperwork imposes Support Vector Machine (SVM) in a dynamic environment. It has been identified that shifting of the optimum values of two hyper-parameters C (Penalty Parameter) and gamma (Kernel Parameter) in the search space is one of the primary reasons for the performance degradation of SVM in dynamic environment. This paper proposes a novel framework that uses a new optimization module Knowledge-Based-Search (KBS) along with Moth -Flame Optimization (MFO) to optimize C and gamma in a dynamic environment to train SVM efficiently. KBS uses knowledge gathered at various instances of time, which are the bi-products of MFO. MFO in our framework is the base optimization algorithm which works underneath KBS. The experiments have shown that KBS helps in controlling the exponential growth of the time complexity of the optimization process where only MFO is used to optimize C and gamma. Integration of KBS with MFO brings down the time complexity to a large extent. To validate the proposed framework we have used a simulated dynamic environment for profit/loss classification problem for organizations. The experiments have also shown that KBS's integration with MFO outperforms integration of KBS with other modern optimization techniques such as Particle Swarm Optimization (PSO), Multi-Verse Optimization (MVO), Grey-Wolf Optimization (GWO), Cuckoo Search (CS), Whale Optimization Algorithm (WOA), Genetic Algorithm (GA), Fire-Fly Algorithm (FFA) and Salp Swarm Algorithm (SSA).

Keyword:

Support Vector Machine

Hyper-parameters

Model selection

Swarm Intelligence

Moth-Flame Optimization