Accurate monitoring of sedentary behaviour and physical activity is key to investigate their exact role in healthy ageing. To date, accelerometers using cut-off point models are most preferred for this, however, machine learning seems a highly promising future alternative. Hence, the current study compared between cut-off point and machine learning algorithms, for optimal quantification of sede...

The present paper deals with a special Random Forest Data Mining technique, designed to alleviate the significant issue of high dimensionality in volatile and complex domains, such as stock market prediction. Since it has been widely acceptable that media affect the behavior of investors, information from both technical analysis as well as textual data from various on-line financial news resour...

Kıyılar kara ve deniz sınırını oluşturan, belirli bir canlı ekosistemini ihtiva eden alanlardır. Suların iklim değişimine bağlı olarak çekilmesi veya yükselmesi, gelgit hareketleri, tropik ekosistemlerde hava olaylarına meydana gelen fırtına, hortum, kasırga vb. olaylarında, alanlarının karalardan ayrıldığı kıyı çizgisinin belirlenmesi önem arz etmektedir. Bu çalışma kapsamında Sentinel-2A uzak...

The paper focuses on the task of predicting future values of stock market index. Two indices namely CNX Nifty and S&P Bombay Stock Exchange (BSE) Sensex from Indian stock markets are selected for experimental evaluation. Experiments are based on 10 years of historical data of these two indices. The predictions are made for 1–10, 15 and 30 days in advance. The paper proposes two stage fusion app...

Machine Learning can be divided into two schools of thought: generative model learning and discriminative model learning. While the MCS community has been focused mainly on the latter, our paper is concerned with questions that arise from ensembles of generative models. Generative models provide us with neat ways of thinking about two interesting learning issues: model selection and semi-superv...

The authorship attribution literature demonstrates the difficulty to design classifiers that outperform simple strategies such as linear classifiers operating on bag of features representation of documents. To overcome this difficulty we propose to use Bagging techniques that rely on learning classifiers on different random subsets of features, then to combine their decision by making them vote...

When intense redaction is needed to protect data subjects’ confidentiality, statistical agencies can release synthetic data, in which identifying or sensitive values are replaced with draws from statistical models estimated from the confidential data. Specifying accurate synthesis models can be a difficult and labor intensive task with standard parametric approaches. We describe and empirically...

Rotation Forest is a recently proposed method for building classifier ensembles using independently trained decision trees. It was found to be more accurate than bagging, AdaBoost and Random Forest ensembles across a collection of benchmark data sets. This paper carries out a lesion study on Rotation Forest in order to find out which of the parameters and the randomization heuristics are respon...

This article has been compiled to identify the best model of housing price forecasting using machine learning methods with maximum accuracy and minimum error. Five important machine learning algorithms are used to predict housing prices, including Nearest Neighbor Regression Algorithm (KNNR), Support Vector Regression Algorithm (SVR), Random Forest Regression Algorithm (RFR), Extreme Gradient B...