A SYSTEM AND METHOD WITH ENHANCED SSD ALGORITHM OPTIMIZATION TECHNIQUE FOR FINDING HYPERPARAMETERS OF SVM

Abstract

[027] The present invention relates to a System and Method with Enhanced SSD Algorithm Optimization Technique for Finding Hyperparameters of SVM. An Enhanced SSD Algorithm for optimizing hyperparameters in Support Vector Machines is disclosed. This system leverages hybrid swarm intelligence, gradient-based differentiation, and adaptive parameter adjustment to locate optimal hyperparameters with high accuracy and efficiency. The algorithm uses multi-objective optimization to balance model accuracy and computational costs, enabling faster convergence and greater resilience against local optima. Designed for scalability, this method is well-suited for large, high-dimensional datasets, providing a robust solution for SVM hyperparameter tuning. Accompanied Drawing [FIG. 1]

Specification

Description:[015] While the present invention is described herein by way of example using embodiments and illustrative drawings, those skilled in the art will recognize that the invention is not limited to the embodiments of drawing or drawings described and are not intended to represent the scale of the various components. Further, some components that may form a part of the invention may not be illustrated in certain figures, for ease of illustration, and such omissions do not limit the embodiments outlined in any way. It should be understood that the drawings and detailed description thereto are not intended to limit the invention to the particular form disclosed, but on the contrary, the invention is to cover all modifications, equivalents, and alternatives falling within the scope of the present invention as defined by the appended claims. As used throughout this description, the word "may" is used in a permissive sense (i.e. meaning having the potential to), rather than the mandatory sense, (i.e. meaning must). Further, the words "a" or "an" mean "at least one" and the word "plurality" means "one or more" unless otherwise mentioned. Furthermore, the terminology and phraseology used herein is solely used for descriptive purposes and should not be construed as limiting in scope. Language such as "including," "comprising," "having," "containing," or "involving," and variations thereof, is intended to be broad and encompass the subject matter listed thereafter, equivalents, and additional subject matter not recited, and is not intended to exclude other additives, components, integers or steps. Likewise, the term "comprising" is considered synonymous with the terms "including" or "containing" for applicable legal purposes. Any discussion of documents, acts, materials, devices, articles and the like is included in the specification solely for the purpose of providing a context for the present invention. It is not suggested or represented that any or all of these matters form part of the prior art base or are common general knowledge in the field relevant to the present invention.
[016] In this disclosure, whenever a composition or an element or a group of elements is preceded with the transitional phrase "comprising", it is understood that we also contemplate the same composition, element or group of elements with transitional phrases "consisting of", "consisting", "selected from the group of consisting of, "including", or "is" preceding the recitation of the composition, element or group of elements and vice versa.
[017] The present invention is described hereinafter by various embodiments with reference to the accompanying drawings, wherein reference numerals used in the accompanying drawing correspond to the like elements throughout the description. This invention may, however, be embodied in many different forms and should not be construed as limited to the embodiment set forth herein. Rather, the embodiment is provided so that this disclosure will be thorough and complete and will fully convey the scope of the invention to those skilled in the art. In the following detailed description, numeric values and ranges are provided for various aspects of the implementations described. These values and ranges are to be treated as examples only and are not intended to limit the scope of the claims. In addition, a number of materials are identified as suitable for various facets of the implementations. These materials are to be treated as exemplary and are not intended to limit the scope of the invention.
System Overview
[018] The Enhanced SSD Algorithm is designed as a robust optimization technique to locate optimal hyperparameters in SVM models. It is implemented as a multi-module system that applies swarm intelligence, gradient-based differentiation, and dynamic adjustments to rapidly search for and refine hyperparameter settings. The architecture of the system includes the following key modules:
Hybrid Swarm Intelligence Module: This module initiates and guides the swarm-based search, leveraging multiple swarm behaviors that emulate biological systems. The swarm agents (particles) adjust their movements based on individual and global information, improving the search balance between exploration and exploitation.
Gradient-Based Differentiation Module: The algorithm integrates gradient information from the objective function (i.e., the SVM's classification accuracy) to direct particle trajectories toward regions of high performance. This module combines aspects of backpropagation-inspired gradient descent with swarm dynamics, improving convergence rates and accuracy.
Dynamic Parameter Adjustment Module: This component adjusts search parameters, such as particle velocity and learning rate, based on real-time feedback from the search process. Adaptive parameter tuning ensures that the search remains efficient and that particles avoid local minima by modulating their behavior in response to changing gradients.
Components and Methodology
a. Hybrid Swarm Intelligence Module
[019] The Hybrid Swarm Intelligence Module initializes a population of particles, each representing a potential solution within the hyperparameter space. Particles start with random initial positions, corresponding to a unique set of SVM hyperparameters.
Particle Dynamics: Each particle's position is updated iteratively according to a hybrid swarm strategy, combining behaviors like PSO's "personal best" approach with differential evolution techniques. This mixture provides a balanced trade-off between exploiting known good regions and exploring new areas.
Diverse Swarm Behaviors: Different groups of particles are assigned specific behaviors, which may include local exploration, global search, and adaptive neighborhood selection, depending on their current positions relative to known optima.
b. Gradient-Based Differentiation Module
[020] Gradient-based differentiation provides the Enhanced SSD Algorithm with refined directional information. In this module:
Gradient Calculation: The algorithm calculates gradients with respect to each hyperparameter (e.g., 𝐶 C and gamma) based on the objective function, which evaluates SVM accuracy for the current particle's parameters.

Trajectory Adjustment: By integrating gradient data, particles move directly toward areas with higher accuracy scores. This gradient-based guidance reduces the number of iterations needed for convergence by enabling faster, more precise adjustments to particle paths.
Backpropagation-Inspired Approach: The gradient-based differentiation in Enhanced SSD takes inspiration from backpropagation by calculating partial derivatives of the objective function relative to each hyperparameter. This approach helps fine-tune particle positions and accelerates convergence.
c. Dynamic Parameter Adjustment Module
[021] The Dynamic Parameter Adjustment Module improves the efficiency of the Enhanced SSD by modifying search parameters over time based on performance data:
Adaptive Velocity Adjustment: Particle velocities are adjusted in response to changes in the objective function gradient. Particles move more aggressively toward regions of improvement early in the search process and gradually slow as they approach an optimal solution.
Real-Time Learning Rate Tuning: The learning rate of each particle is dynamically modulated based on feedback from the objective function. This helps prevent premature convergence and allows the algorithm to continue exploring the solution space effectively.
d. Multi-Objective Optimization Framework
[022] The Enhanced SSD Algorithm incorporates a multi-objective approach, allowing users to optimize not only accuracy but also other factors such as computational efficiency and model complexity. This multi-objective capability is particularly beneficial in applications where model accuracy must be balanced with real-time constraints.
Implementation and Workflow
[023] The Enhanced SSD Algorithm follows a structured process to optimize SVM hyperparameters:
Initialization: Particles are initialized within the hyperparameter space, with initial values for 𝐶 C and gamma chosen at random within specified bounds.
Objective Function Evaluation: Each particle's SVM model is evaluated on a validation set, and the accuracy is recorded as the objective function.
Swarm-Based Movement and Gradient Updates: The Hybrid Swarm Intelligence Module updates each particle's position based on gradient and swarm behavior updates, as described previously.
Dynamic Parameter Adjustment: The algorithm adjusts particle velocities and learning rates as needed, ensuring the search remains balanced and avoids local minima.
Convergence Check: The algorithm iterates until the maximum iterations are reached or the objective function improvement falls below a threshold.
[024] It is to be understood that the above description is intended to be illustrative, and not restrictive. For example, the above-discussed embodiments may be used in combination with each other. Many other embodiments will be apparent to those of skill in the art upon reviewing the above description.
[025] The benefits and advantages which may be provided by the present invention have been described above with regard to specific embodiments. These benefits and advantages, and any elements or limitations that may cause them to occur or to become more pronounced are not to be construed as critical, required, or essential features of any or all of the embodiments.
[026] While the present invention has been described with reference to particular embodiments, it should be understood that the embodiments are illustrative and that the scope of the invention is not limited to these embodiments. Many variations, modifications, additions and improvements to the embodiments described above are possible. It is contemplated that these variations, modifications, additions and improvements fall within the scope of the invention. , Claims:1.A system for optimizing hyperparameters of a Support Vector Machine, comprising a Hybrid Swarm Intelligence Module configured to search a hyperparameter space using swarm-based dynamics.

2.The system of Claim 1, wherein the Hybrid Swarm Intelligence Module incorporates diverse swarm behaviors to balance exploration and exploitation within the search space.

3.The system of Claim 1, further comprising a Gradient-Based Differentiation Module, configured to calculate and apply gradients of the objective function with respect to each hyperparameter.

4.The system of Claim 1, further comprising a Dynamic Parameter Adjustment Module, configured to modify particle velocities and learning rates based on feedback from the objective function.

5.A method for optimizing Support Vector Machine hyperparameters, comprising initializing particles in a hyperparameter space, evaluating an objective function, applying gradient-based differentiation, and adjusting parameters dynamically.

Documents