Boosted Hierarchical Adaptive Activation Network: A Hybrid Approach to Machine Learning

Introduction

In the ever-evolving world of machine learning, researchers and developers are continually pushing the boundaries to create more efficient and powerful models. Today, I introduce my new hybrid approach: the Boosted Hierarchical Adaptive Activation Network (BHAAN).

This innovative model combines the strengths of neural networks with adaptive activation functions, hierarchical layers, and gradient boosting. Let’s walk through the details of this model, its implementation, and its impressive performance on the Iris dataset.

What is BHAAN?

The Boosted Hierarchical Adaptive Activation Network (BHAAN) is an advanced machine learning model designed to leverage the best of both neural networks and gradient boosting. Here’s what sets it apart:

1. Adaptive Activation Functions: These functions dynamically adjust during training to optimize the performance of the neural network.
2. Hierarchical Layers with Dynamic Granularity: The model includes multiple levels of layers, combining features from different levels to capture complex patterns in the data.
3. Gradient Boosting: Utilizes the power of XGBoost to enhance the performance of the…