Bankruptcy prediction has attracted a lot of research interests in previous literature, and recent studies have shown that artificial neural networks (ANN) method achieved better performance than traditional statistical ones. ANN approaches have, however, suffered from difficulties with generalization, producing models that can overfit the data. This paper employs a relatively new machine learn...

In this paper a novel architecture of Support Vector Machine classifiers utilizing binary decision tree (SVM-BDT) for solving multiclass problems is presented. The hierarchy of binary decision subtasks using SVMs is designed with a clustering algorithm. For consistency between the clustering model and SVM, the clustering model utilizes distance measures at the kernel space, rather than at the i...

A test was designed that apparently examined a student’s knowledge of assignment and sequence before a first course in programming but in fact was designed to capture their reasoning strategies. An experiment found two distinct populations of students: one could build and consistently apply a mental model of program execution; the other appeared either unable to build a model or to apply one co...

Feature selection has proved to be an effective way to reduce the model complexity while giving a relatively desirable accuracy, especially, when data is scarce or the acquisition of some feature is expensive. However, the single selected model may not always generalize well for unseen test data whereas other models may perform better. Bayesian Model Averaging (BMA) is a widely used approach to...

Obstructive sleep apnea (OSA) is a general sleep disorder and is a significant cause of motor vehicle crashes and chronic diseases. The severity of the respiratory events is measured by the frequency and duration of apneas and hypopneas per hour of sleep, namely apnea-hypopnea index (AHI), using polysomnography (PSG). Suspected patients can be classified as normal (AHI<5), mild (5AHI<15), mode...

Classification performance of support vector machine (SVM) will be influenced by its model parameters. For this problem, a new method named magnetic bacteria optimization algorithm (MBOA) that optimizes the parameters of SVM is proposed. It is tested by the UCI standard data sets and compared with the other optimization algorithms, such as particle swarm optimization (PSO). Experimental results...

The support vector machine (SVM), as a novel type of learning machine, was used to develop a Quantitative Structure-Property Relationship (QSPR) model of the O-H bond dissociation energy (BDE) of 78 substituted phenols. The six descriptors calculated solely from the molecular structures of compounds selected by forward stepwise regression were used as inputs for the SVM model. The root-mean-squ...

In this work we address the Eν–SVM model proposed by Pérez–Cruz et al. as an extension of the traditional ν support vector classification model (ν–SVM). Through an enhancement of the range of admissible values for the regularization parameter ν, the Eν–SVM has been shown to be able to produce a wider variety of decision functions, giving rise to a better adaptability to the data. However, while...

bearing capacity prediction of shallow foundation is one of the most important problems in geotechnical engineering practices, with a wide variety range of methods which have been introduced to forecast it accurately. recently, soft computing methods such as artificial neural networks (anns) and support vector machines (svms) have been used for prediction of the ultimate bearing capacity of sha...