A dynamic annealing learning for PLSOM neural networks: Applications in medicine and applied sciences

Abstract

In recent years, the field of unsupervised learning in neural networks has witnessed significant advancements. This innovative learning technique holds great promise for applications in diverse domains, with particular significance in the realms of medicine and applied sciences such as medical image analysis, drug discovery, predictive analytics, and pattern recognition. The neighborhood function plays a crucial role in the Improved Parameter-Less Self-Organizing Map (PLSOM2) algorithm by governing the rate of change in the vicinity of the winning neuron. During learning iterations, the neighborhood size is dynamically adjusted to encompass the activated neighboring neurons relative to the winning neuron. The training process begins with a larger neighborhood radius, promoting rough ordering, and gradually refines the process by reducing the radius for fine-tuning. This dynamic neighborhood size significantly influences the final training outcome of the PLSOM2 algorithm. However, one of the major bottlenecks of the PLSOM2 algorithm has been the slow ordering time and the challenge of determining an optimal neighborhood size. These issues often lead to topological defects during training, such as kinks or warps in the output maps. Merely increasing processing time has proven insufficient to overcome these challenges. In this paper, we propose a novel dynamic neighborhood function designed to accelerate the convergence process of the PLSOM2 algorithm, achieving the best shape and adaptation of the neighborhood width. The study demonstrates that by improving the neighborhood function of the PLSOM2 algorithm, map distortion can be effectively suppressed. Importantly, this enhancement enables the algorithm to handle network size, neighborhood size, and the large dimensional output space adeptly. It adaptively decreases the neighborhood size over time, ensuring convergence while appropriately managing network growth and avoiding twisting and misconfiguration. To assess the effectiveness of the proposed method, we conducted an extensive set of experiments across eight real-world benchmark datasets. Notably, the outcomes of these experiments are presented showcases the results of paired t-tests, highlighting the consistency and robustness of the proposed algorithm's performance. Despite non-significant p-values in many cases, the algorithm consistently excels across various datasets, underlining its practical significance. On the other hand, presents the results of One-way Analysis of Variance (ANOVA) tests. These results further reinforce the consistency of the performance of algorithm, as indicated by p-values exceeding the common significance threshold of 0.05. The combined findings from both tables provide strong statistical evidence of the proposed algorithm's robustness and effectiveness across diverse datasets. The proposed research introduces a dynamic neighborhood function that not only improves the PLSOM2 algorithm's convergence but also enhances its adaptability and topological preservation. These enhancements, supported by the statistical tests results, underscore the algorithm's practical significance and its suitability for real-world applications, where statistical significance may not always capture the full extent of its capabilities.