Objective: In recent years, ensemble learning methods have gained widespread use for early diagnosis of cancer diseases. this study, it is aimed to establish a high-performance model and classification renal cell carcinomas.Methods: the hemogram laboratory data 140 patients with carcinoma without were included in study. The set includes 27 predictors 1 dependent variable. obtained retrospective...

Ensemble learning is a machine learning approach that utilises a number of classifiers to contribute via voting to identifying the class label for any unlabelled instances. Random Forests RF is an ensemble classification approach that has proved its high accuracy and superiority. However, most of the commonly used selection methods are static. Motivated by the idea of having self-optimised RF c...

A wrapper variable selection procedure is proposed for use with learning machines that generate a measure of variable importance, such as Random Forest. The procedure is based on iteratively removing low-ranking variables and assessing the learning machine performance by cross-validation. The procedure is implemented for Random Forest on some QSAR modeling examples from drug discovery and devel...

Machine learning with classification can effectively be applied for many applications, especially those with complex measurements. Therefore classification technique can be used for prediction of diseases like cancer, liver disorders and heart disease etc which involve complex measurements. This is part of growing demand and much interesting towards predictive diagnosis. It has also been establ...

The classification of documents is one the problems studied since ancient times and still continues to be studied. With social media becoming a part daily life its misuse, importance text has started increase. This paper investigates effect data augmentation with sentence generation on performance in an imbalanced dataset. We propose LSTM based method, Term Frequency-Inverse Document Frequency ...

Recently there has been a lot of interest in “ensemble learning” — methods that generate many classifiers and aggregate their results. Two well-known methods are boosting (see, e.g., Shapire et al., 1998) and bagging Breiman (1996) of classification trees. In boosting, successive trees give extra weight to points incorrectly predicted by earlier predictors. In the end, a weighted vote is taken ...

Network traffic classification continues to be an interesting subject among numerous networking communities. This method introduces multi-beneficial solutions in different avenues, such as network security, network management, anomaly detection, and quality-of-service. In this paper, we propose a supervised machine learning method that efficiently classifies different types of applications usin...