Data mining is defined as the extraction of hidden predictive information from large databases. It finds its application in real world situations such as business, science, technology, and government .A data mining algorithm constitutes a model, a preference criterion, and a search algorithm. The more common model functions in data mining include classification, clustering, rule generation and ...

Cost overruns are more common in infrastructure projects especially, more common in road construction activities. There existed a need to develop a probabilistic cost overrun analysis model in construction projects as a decision support tool for contractors before the bidding stage. The objective of this study is to identify the critical factors affecting cost overrun and obtain statistical mod...

In machine learning, symbolic approaches usually yield comprehensible results without free parameters for further (incremental) retraining. On the other hand, non-symbolic (connectionist or neural network based) approaches usually yield black-boxes which are diicult to understand and reuse. The goal of this study is to propose a machine learner that is both incrementally retrainable and compreh...

We compare the use of three algorithms for performing automated morphological galaxy classiÐcation using a sample of 800 galaxies. ClassiÐers are created using a single training set as well as bootstrap replicates of the training set, producing an ensemble of classiÐers. We use a Naive Bayes classiÐer, a neural network trained with backpropagation, and a decision-tree induction algorithm with p...

This paper presents a novel fast algorithm to construct feedforward neural networks for pattern classification tasks. The algorithm constructs the neural network by adding elementary neural elements to the first layer, in response to the distribution of the patterns in the training set. Each elementary neural element of the network is trained with different pattern subsets and forms a hyper-pla...

This paper presents a new model for predicting the compressive strength of steel-confined concrete on circular concrete filled steel tube (CCFST) stub columns under axial loading condition based on Artificial Neural Networks (ANNs) by using a large wide of experimental investigations. The input parameters were selected based on past studies such as outer diameter of column, compressive strength...

Despite all the success that deep neural networks have seen in classifying certain datasets, the challenge of finding optimal solutions that generalize well still remains. In this paper, we propose the Boundary Optimizing Network (BON), a new approach to generalization for deep neural networks when used for supervised learning. Given a classification network, we propose to use a collaborative g...