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...

We recently introduced the Random Forest Recursive Feature Elimination (RF-RFE) algorithm for feature selection. In this paper we apply it to the identification of relevant features in the spectra (fingerprints) produced by Proton Transfer Reaction Mass Spectrometry (PTR-MS) analysis of four agro-industrial products (two datasets with cultivars of Berries and other two with typical cheeses, all...

MOTIVATION The advent of high-throughput genomics has produced studies with large numbers of predictors (e.g. genome-wide association, microarray studies). Machine learning algorithms (MLAs) are a computationally efficient way to identify phenotype-associated variables in high-dimensional data. There are important results from mathematical theory and numerous practical results documenting their...

Efficient fault diagnosis of power transformer can effectively ensure the safe and stable operation system. Considering that effect traditional Random Forest (RF) is seriously affected by initial parameters, this paper proposes a RF algorithm based on Grey Wolf Optimization (GWO-RF) to improve accuracy identification. The uses GWO optimize total number decision trees depth maximum tree, balanci...

We study the number of causal variants and associated regions identified by top SNPs in rankings given by the popular 1 df chi-squared statistic, support vector machine (SVM) and the random forest (RF) on simulated and real data. If we apply the SVM and RF to the top 2r chi-square-ranked SNPs, where r is the number of SNPs with P-values within the Bonferroni correction, we find that both improv...

Heart disease is a leading cause of death worldwide, and the need for effective predictive systems major source to treat affected patients. This study aimed determine how improve accuracy Random Forest in predicting classifying heart disease. The experiments performed this were designed select most optimal parameters using an RF optimization technique GA. Genetic Algorithm (GA) used optimize pr...

In this paper, we propose a new Random Forest (RF) based ensemble method, ForesTexter, to solve the imbalanced text categorization problems. RF has shown great success in many real-world applications. However, the problem of learning from text data with class imbalance is a relatively new challenge that needs to be addressed. A RF algorithm tends to use a simple random sampling of features in b...

Above-ground biomass and carbon stock are fundamental components of the global cycle, essential for climate change mitigation. Remote sensing data can provide timely accurate estimates various forest attributes, especially over large remote forested areas. The objective this research was to investigate potential multispectral LiDAR estimating stem (SB) total (TB) in a multi-layered fir using an...

Forest environment classification in mountain regions based on single-sensor remote sensing approaches is hindered by forest complexity and topographic effects. Temperate broadleaf forests in western Asia such as the Hyrcanian forest in northern Iran have already suffered from intense anthropogenic activities. In those regions, forests mainly extend in rough terrain and comprise different stand...

Random Forest (RF) is an ensemble supervised machine learning technique that was developed by Breiman over a decade ago. Compared with other ensemble techniques, it has proved its accuracy and superiority. Many researchers, however, believe that there is still room for enhancing and improving its performance accuracy. This explains why, over the past decade, there have been many extensions of R...