This paper proposes a new methodology for designing Fuzzy Cognitive Maps using crisp decision trees that have been fuzzified. Fuzzy cognitive map is a knowledge-based technique that works as an artificial cognitive network inheriting the main aspects of cognitive maps and artificial neural networks. Decision trees, in the other hand, are well known intelligent techniques that extract rules from...

Selecting the close-to-optimal collective algorithm based on the parameters of the collective call at run time is an important step in achieving good performance of MPI applications. In this paper, we explore the applicability of C4.5 decision trees to the MPI collective algorithm selection problem. We construct C4.5 decision trees from the measured algorithm performance data and analyze the de...

Breast cancer is the second major cause of deaths in women. Machine learning classification techniques can be used to increase precision diagnosis and bring it closer 100%, thus saving lives many people. This paper proposed four different models, built using combinations selected features applying five ML all models identify best model with highest accuracy. It analyzes machine techniques, name...

In this study, we introduce a novel denoising transformer-based neural network (DTNN) model for predicting the remaining useful life (RUL) of lithium-ion batteries. The proposed DTNN significantly outperforms traditional machine learning models and other deep architectures in terms accuracy reliability. Specifically, achieved an R2 value 0.991, mean absolute percentage error (MAPE) 0.632%, RUL ...

In this paper we experimentally compare the classification uncertainty of the randomised Decision Tree (DT) ensemble technique and the Bayesian DT technique with a restarting strategy on a synthetic dataset as well as on some datasets commonly used in the machine learning community. For quantitative evaluation of classification uncertainty, we use an Uncertainty Envelope dealing with the class ...

We study a time series model that can be viewed as a decision tree with Markov temporal structure. The model is intractable for exact calculations, thus we utilize variational approximations . We consider three different distributions for the approximation: one in which the Markov calculations are performed exactly and the layers of the decision tree are decoupled, one in which the decision tre...

Decision Trees are considered to be one of the most popular approaches for representing classifiers. Researchers from various disciplines such as statistics, machine learning, pattern recognition, and Data Mining have dealt with the issue of growing a decision tree from available data. This paper presents an updated survey of current methods for constructing decision tree classifiers in a top-d...