The purpose of this paper is to propose a hybrid model which combines locally linear embedding (LLE) algorithm and support vector machines (SVM) to predict the failure of firms based on past financial performance data. By making use of the LLE algorithm to perform dimension reduction for feature...

Fault diagnosis for wind turbine transmission systems is an important task for reducing their maintenance cost. However, the non-stationary dynamic operating conditions of wind turbines pose a challenge to fault diagnosis for wind turbine transmission systems. In this paper, a novel fault...

Convex optimization methods are used for many machine learning models such as support vector machine. However, the requirement of a convex formulation can place limitations on machine learning models. In recent years, a number of machine learning methods not requiring convexity have emerged. In...

Retailers are frequently uncertain about the underlying demand distribution of a new product. When taking the empirical Bayesian approach of Scarf (1959), they simultaneously stock the product over time and learn about the distribution. Assuming that unmet demand is lost and unobserved, this...

We consider the assignment of enterprise applications in virtual machines to physical servers, also known as server consolidation problem. Data center operators try to minimize the number of servers, but at the same time provide sufficient computing resources at each point in time. While...

This work examines an artificial neural system (ANS) capable of dimensionality-reduction and its fitness to a business data analysis problem. While ANSs are often used in the later stages of explorative data analysis to place similar cases in clusters or to identify known patterns, the role of...

In this paper we propose two distinct ways of augmenting the existing clustering environment so that granular data (patterns) can be accommodated. The two approaches deal with either holistic or atomistic representations of metrics and descriptors when mining outcomes of granule-valued random...

Frequent pattern evaluation is imperative for cricket match data to develop more proficient coaching strategies and progress the performance of individual players. The rapid growth in size of the match database far exceeds the human ability to analyse, thus creating an opportunity to extract...