Mining high dimensional biomedical data with existing classifiers is challenging and the predictions are often inaccurate. We investigated the use of Bayesian Logistic Regression (B-LR) for mining such data to predict and classify various disease conditions. The analysis was done on twelve biomedical datasets with binary class variables and the performance of B-LR was compared to those from oth...

Case-based reasoning (CBR) systems use similarity functions to solve new problems with past situations. K-nearest neighbors algorithm (K-NN) have been used in CBR systems to define new cases status according to characteristics of past nearest cases. We proposed a new hybrid approach combining logistic regression (LR) with K-NN to optimize CBR classification. First, we analyzed the knowledge dat...

the purpose of this study is presenting a model forecasting financial crisis in tehran stock exchange listed companies. to do this, productive firms that had been accepted in tehran stock exchange between 2002 and 2009, were selected as the study sample. first the independent variables were obtained based on financial ratios and then based on article 141 of the law of commerce, the insolvent an...

Most standard learning algorithms, such as Logistic Regression (LR) and the Support Vector Machine (SVM), are designed to deal with i.i.d. (independent and identically distributed) data. They therefore do not work effectively for tasks that involve non-i.i.d. data, such as “region segmentation”. (Eg, the “tumor vs non-tumor” labels in a medical image are correlated, in that adjacent pixels typi...

Auditors face the difficult task of detecting companies that issue manipulated financial statements. In recent years, machine learning methods have provided a feasible solution to this task. This study develops support vector machine (SVM) models using published South African financial data. The input vectors are comprised of ratios derived from financial statements. The three SVM models are co...

A challenge in estimating students’ changing knowledge from sequential observations of their performance arises when each observed step involves multiple subskills. To overcome this mismatch in grain size between modelled skills and observed actions, we use logistic regression over each step’s subskills in a dynamic Bayes net (LR-DBN) to model transition probabilities for the overall knowledge ...

Feature selection methods are essential to identify a subset of features that improve the prediction performance of subsequent classification models and thereby also simplify their interpretability. Preceding studies showed the defectiveness in terms of specific biases of single feature selection methods, whereas an ensemble of feature selection techniques has the advantage to alleviate and com...

Emerging markets contain the vast majority of the world’s population. Despite the huge number of inhabitants, these markets still lack a proper finance infrastructure. One of the main difficulties felt by customers is the access to loans. This limitation arises from the fact that most customers usually lack a verifiable credit history. As such, traditional banks are unable to provide loans. Thi...

Traditional way of conducting analyses of human behaviors is through manual observation. For example in couple therapy studies, human raters observe sessions of interaction between distressed couples and manually annotate the behaviors of each spouse using established coding manuals. Clinicians then analyze these annotated behaviors to understand the effectiveness of treatment that each couple ...