Rough set plays vital role to overcome the complexities, vagueness, uncertainty, imprecision, and incomplete data during features analysis. Classification is tested on certain dataset that maintain an exact class and review process where key attributes decide the class positions. To assess efficient and automated learning, algorithms are used over training datasets. Generally, classification is...

Feedforward neural networks (FFNN) have been utilised for various research in machine learning and they have gained a significantly wide acceptance. However, it was recently noted that the feedforward neural network has been functioning slower than needed. As a result, it has created critical bottlenecks among its applications. Extreme Learning Machines (ELM) were suggested as alternative learn...

Extreme learning machine (ELM) has been an important research topic over the last decade due to its high efficiency, easy-implementation, unification of classification and regression, and unification of binary and multi-class learning tasks. Though integrating these advantages, existing ELM algorithms pay little attention to optimizing the choice of kernels, which is indeed crucial to the perfo...

Recently, a new learning algorithm for the feedforward neural network named the extreme learning machine (ELM) which can give better performance than traditional tuning-based learning methods for feedforward neural networks in terms of generalization and learning speed has been proposed by Huang et al. In this paper, we first extend the ELM algorithm from the real domain to the complex domain, ...

Monotonic classification problems mean that both feature values and class labels are ordered and monotonicity relationships exist between some features and the decision label. Extreme Learning Machine (ELM) is a singlehidden layer feedforward neural network with fast training rate and good generalization capability, but due to the existence of training error, ELM cannot be directly used to hand...

Unlike the conventional neural network theories and implementations, Huang et al. [Universal approximation using incremental constructive feedforward networks with random hidden nodes, IEEE Transactions on Neural Networks 17(4) (2006) 879–892] have recently proposed a new theory to show that single-hidden-layer feedforward networks (SLFNs) with randomly generated additive or radial basis functi...

Rough Set Theory Paw82, Paw91] is a framework for reasonably dealing with imprecise or uncertain data. It can be used to implement application independent symbolic machine learning techniques. A special application of a Rough Set based machine learning algorithm is presented that can predict german word stress by extracting symbolic rules from sample data. A comparison is made between the predi...

This paper proposes a novel method for supervised subspace learning based on Single-hidden Layer Feedforward Neural networks. The proposed method calculates appropriate network target vectors by formulating a Bayesian model exploiting both the labeling information available for the training data and geometric properties of the training data, when represented in the feature space determined by t...

Extreme learning machines (ELMs) are a versatile machine technique that can be seamlessly implemented with optical systems. In short, they described as network of hidden neurons random fixed weights and biases, generate complex behaviour in response to an input. Yet, despite the success physical implementations ELMs, there is still lack fundamental understanding about their implementations. Thi...

In this work, an attempt has been made to analyze human femur radiographic bone images using sharpness features and learning models. The sharpness features are derived for the neck of the femur bone images to characterize the trabecular structure. The significant parameters are found using Independent component analysis (ICA) and Principal Component Analysis (PCA). The first three most signific...