Precision Medicine is an emerging approach for prevention and treatment of disease that considers individual variability in genes, environment, and lifestyle for each person. The dissemination of individualized evidence by automatically identifying population information in literature is a key for evidence-based precision medicine at the point-of-care. We propose a hybrid approach using natural...

This paper is focused on studying how data privacy could be preserved with fuzzy rule bases as interpretable as possible. These fuzzy rule bases are obtained from a data mining strategy based on building a decision tree. The antecedents of each rule produced by these systems contain information about the released variables (quasiidentifier) whereas the consequent contains information only about...

Fuzzy logic techniques are efficient in solving complex, ill-defined problems that are characterized by uncertainty of environment and fuzziness of information. Fuzzy logic allows handling uncertain and imprecise knowledge and provides a powerful framework for reasoning. Fuzzy reasoning models are relevant to a wide variety of subject areas such as engineering, economics, psychology, sociology,...

Rule Based Detection Systems have been successful in preventing attacks on network resources, but suffer a problem in that they are not adaptable in cases where new attacks are made i.e. they need human intervention for investigating new attacks. This paper proposes the creation of a predictive intrusion detection model that is based on usage of classification techniques such as decision tree, ...

In this paper, we are exploring a panel of classifier response to an imbalanced medical data set. In this work we are using LIDC (Lung Image Database Consortium) dataset, which is a very good example for imbalanced data. The main objective of this work is to examine how the response of different categories of classifier is, when subjected to imbalanced dataset. We are considering five categorie...

In this paper, we are exploring the response of individual classifier families on imbalanced medical data. In this work we are using LIDC (Lung Image Database Consortium) dataset, which is a very good example for imbalanced data. The main objective of this work is to examine how will be the response of different categories of classifier on imbalanced dataset. We are considering five categories ...

Text Classification is an important research field in information retrieval and text mining. The main task in text classification is to assign text documents in predefined categories based on documents’ contents and labeled-training samples. Since word detection is a difficult and time consuming task in Persian language, Bayesian text classifier is an appropriate approach to deal with different...

We applied the novel bootstrap 632 b rule to choose tree-based classifiers trained for modeling the risk of parasite presence in a host population of ungulates. The method is designed to control overfitting: compact classification trees (CART) are selected using a nonlinear combination of the resubstitution error and the standard bootstrap error estimate. Model selection based on the 632 b rule...

Fuzzy Classifiers are an powerful class of fuzzy systems. Evolving fuzzy classifiers from numerical data has assumed lot of remarks in the recent past. This paper proposes a method of evolving fuzzy classifiers using a three step technique. In the first step, a modified Fuzzy C–Means Clustering technique is applied to generate membership functions. In the next step, rule base are generated usin...

Recently, the interest in data-driven approaches to the modeling of nonlinear processes has increased. Techniques based on fuzzy sets and rule-based systems have proven suitable mainly because of their potential to yield transparent models that are at the same time reasonably accurate. Many of the data-driven fuzzy modeling algorithms, however, aim primarily at good numerical approximation, whi...