نتایج جستجو برای: using shannons entropy and random forest models
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Given a branching random walk, let Mn be the minimum position of any member of the n’th generation. We calculate EMn to within O(1) and prove exponential tail bounds for P {|Mn − EMn| > x}, under quite general conditions on the branching random walk. In particular, together with work of [8], our results fully characterize the possible behavior of EMn when the branching random walk has bounded b...
Genomic selection is a promising challenge for discovering genetic variants influencing quantitative and threshold traits for improving the genetic gain and accuracy of genomic prediction in animal breeding. Since a proportion of genotypes are generally uncalled, therefore, prediction of genomic accuracy requires imputation of missing genotypes. The objectives of this study were (1) to quantify...
In this paper, we apply five machine learning models (Logistic Regression, Naive Bayes, LinearSVC, SVM with linear kernel and Random Forest) and three feature selection techniques (PCA, RFE and Heatmap) in one of the key procedures for breast cancer diagnosis. Using the biopsy cytopathology data with 30 numerical features, we achieve a high accuracy of 97.8%. We further compare performances of ...
High-dimensional supervised learning problems, e.g. in image exploitation and bioinformatics, are more frequent than ever. Tree-based ensemble methods, such as random forests (Breiman, 2001) and extremely randomized trees (Geurts et al., 2006), are effective variance reduction techniques offering in this context a good trade-off between accuracy, computational complexity, and interpretability.
the present study sought to investigate the impact of using mind-mapping technique instruction on female elementary efl learners reading comprehension; it also investigated their attitudes towards using mind-mapping technique as a tool to improve their reading comprehension. this study followed a quasi-experimental design with two intact groups as experimental, and control groups. the participa...
Random Forest regression (RF), Partial-Least-Squares (PLS) regression, Support Vector Machines (SVM), and Artificial Neural Networks (ANN) were used to develop QSPR models for the prediction of aqueous solubility, based on experimental data for 988 organic molecules. The Random Forest regression model predicted aqueous solubility more accurately than those created by PLS, SVM, and ANN and offer...
1- Introduction The gully erosion occurrence, due to the high rate of sediment production in the watershed, is one of the problems of natural resources management in the context of soil management and protection. It is known as an important signature of land degradation and forming as well as a source of sediment in a range of environments. Gully erosion often has severe environmental and econ...
This investigation developed models to estimate aspects of physical activity and sedentary behavior from three-axis high-frequency wrist-worn accelerometer data. The models were developed and tested on 20 participants (n = 10 males, n = 10 females, mean age = 24.1, mean body mass index = 23.9), who wore an ActiGraph GT3X+ accelerometer on their dominant wrist and an ActiGraph GT3X on the hip wh...
QUANTIFYING THE EFFECTS OF CORRELATED COVARIATES ON VARIABLE IMPORTANCE ESTIMATES FROM RANDOM FORESTS By Ryan Vincent Kinies A thesis submitted in partial fulfillment of the requirements for the degree of Master of Science at Virginia Commonwealth University. Virginia Commonwealth University, 2006 Major Director: Kellie J. Archer, Ph.D. Assistant Professor, Department of Biostatistics Recent ad...
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