Conditioning Optimization of Extreme Learning Machine by Multitask Beetle Antennae Swarm Algorithm
This work addresses a specific optimization issue in ELM for machine learning applications, representing an incremental improvement.
The paper tackled the ill-posed problem in Extreme Learning Machines (ELM) caused by random input weights and biases by proposing the Multitask Beetle Antennae Swarm Algorithm (MBAS) to optimize conditioning, resulting in reduced condition number and regression error with good generalization performance.
Extreme learning machine (ELM) as a simple and rapid neural network has been shown its good performance in various areas. Different from the general single hidden layer feedforward neural network (SLFN), the input weights and biases in hidden layer of ELM are generated randomly, so that it only takes a little computation overhead to train the model. However, the strategy of selecting input weights and biases at random may result in ill-posed problem. Aiming to optimize the conditioning of ELM, we propose an effective particle swarm heuristic algorithm called Multitask Beetle Antennae Swarm Algorithm (MBAS), which is inspired by the structures of artificial bee colony (ABS) algorithm and Beetle Antennae Search (BAS) algorithm. Then, the proposed MBAS is applied to optimize the input weights and biases of ELM. Experiment results show that the proposed method is capable of simultaneously reducing the condition number and regression error, and achieving good generalization performances.