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This paper discusses the application and benefits of data mining techniques to construct prediction models in the field of corporate bankruptcy. It analyzes a dataset of 120 companies using different data mining techniques. Findings show that neural network is recommended as the best model to predict corporate bankruptcy. Findings also show that the proper use and selection of data mining techniques...
Titanic disaster occurred 100 years ago on April 15, 1912, killing about 1500 passengers and crew members. The fateful incident still compel the researchers and analysts to understand what can have led to the survival of some passengers and demise of the others. With the use of machine learning methods and a dataset consisting of 891 rows in the train set and 418 rows in the test set, the research...
In Machine Learning, we often encounter instances of imbalanced data which occur whenever there is an unequal representation in the classification categories. New found interest in Machine Learning has made its usage ubiquitous. Its applications encompass a wide plethora of scenarios ranging from Business and Banking to Bioinformatics and Psychology. These problems are often characterized by imbalanced...
Active learning aims to selectively label the most informative examples to save the data collection cost. While active learning has been well studied for balanced classification problems, limited research is performed in cost-sensitive scenario. In this paper, we investigate the problem of active learning for cost-sensitive classification. We first propose a general active learning framework named...
Classification of web content is an interesting and widely pursued field of research in machine learning. Web classification could be done in various ways based upon the criteria chosen. Subjective classification involves classification of web pages based upon the subject to which these pages belong (say history, economics, politics, etc.). Another way of classifying web pages could be based upon...
Adaptive automation (AA) has emerged as a viable solution to improving human performance in complex environments. However, understanding when to prompt, pause, and terminate AA remains unclear. Augmenting the user with physiological sensors offers new insight into the user’s state, and thus, offers insight into when and how to implement AA. The research presented investigates the efficacy of prediction...
Radiotherapy is one of the main treatments used against cancer. Radiotherapy uses radiation to destroy cancerous cells trying, at the same time, to minimize the damages in healthy tissues. The planning of a radiotherapy treatment is patient dependent, resulting in a lengthy trial and error procedure until a treatment complying as most as possible with the medical prescription is found. Intensity Modulated...
Recent studies showed that protein-protein interaction network based features can significantly improve the prediction of protein subcellular localization. However, it is unclear whether network prediction models or other types of protein-protein correlation networks would also improve localization prediction. We present NetLoc, a novel diffusion kernel-based logistic regression (KLR) algorithm for...
Book reviews are comments written by readers regarding their experiences about a particular book. Some reviews contain useful information and may help prospective buyers in making a purchase decision, while some are viewed as less helpful, such as, complaints about shipping delay. The review's content is the key to differentiating them. Presenting a methodology for evaluating the helpfulness of a...
Financial distress prediction is an important research topic in both academic and practical world. This paper puts forward a financial distress prediction model based on multiple reduction ensembles, which employs neighborhood rough set based attribute reduction to generate a set of reducts, then each reduct is used to train a base classifier, and finally their results are combined through simple...
Prediction of fault prone software components is one of the most researched problems in software engineering. Many statistical techniques have been proposed but there is no consensus on the methodology to select the "best model" for the specific project. In this paper, we introduce and discuss the merits of cost curve analysis of fault prediction models. Cost curves allow software quality...
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