Publications by Jose Tamez
COVID-19 Mexico
Covid19 En México Este documento muestra una estimación del número de hospitalizados y de muertes debido a COVID-19. Los datos son extraídos de los Datos Abiertos - Dirección General de Epidemiología de la secretaria de salud de México. https://www.gob.mx/salud/documentos/datos-abiertos-152127 Los datos del archivo .csv son importados a R ...
4026 sym R (14003 sym/14 pcs) 54 img
FRESA.CAD ML Methods and Filters
1 FRESA.CAD Classifiers 1.1 GBSG2 op <- par(no.readonly = TRUE,pty="m") data("GBSG2", package = "TH.data") GBSG2 <- GBSG2[complete.cases(GBSG2),] GBSG2$tgrade <- as.character(GBSG2$tgrade) GBSG2$tgrade <- as.factor(GBSG2$tgrade) summary(GBSG2) GBSG2 <- subset(GBSG2,cens == 1 | GBSG2$time > 730) #Only subjects with more than three year ...
323 sym R (32181 sym/24 pcs) 23 img
Feature decorrelation
1 FRESA.CAD Feature Decorrelation 1.1 Sonar set op <- par(no.readonly = TRUE,pty="m") data(Sonar) Sonar$Class <- 1*(Sonar$Class == "M") 1.2 Training and Testing sets par(op) trainSamples <- sample(nrow(Sonar),0.70*nrow(Sonar)) traningSet <- Sonar[trainSamples,]; testingSet <- Sonar[-trainSamples,]; traningSetc <- traningSet traningSetc...
338 sym R (33913 sym/24 pcs) 23 img
FRESA.CAD ML Methods and Filters and Decorrelation
1 FRESA.CAD diabetesifiers 1.1 Diabetes Data Set op <- par(no.readonly = TRUE) data("PimaIndiansDiabetes2", package = "mlbench") PimaIndiansDiabetes <- PimaIndiansDiabetes2[complete.cases(PimaIndiansDiabetes2),] PimaIndiansDiabetes$diabetes <- 1*(PimaIndiansDiabetes$diabetes == "pos") PimaIndiansDiabetes.mat <- as.data.frame(model.matrix(...
342 sym R (35258 sym/25 pcs) 23 img
Melanoma vs Seborrheic Diagnosis
Melanoma vs Seborreic_Keratosis Loading data sets op <- par(no.readonly = TRUE) MelanomaFeatures <- read.csv("E:/SKINCCANCER/MatlabScripts/MelanomaLesionFeatures.csv", header=FALSE) SeborrheicFeatures <- read.csv("E:/SKINCCANCER/MatlabScripts/SeborrheicLesionFeatures.csv", header=FALSE) MelanomaControlFeatures <- read.csv("E:/SKINCCANCER/Mat...
213 sym R (14092 sym/17 pcs) 7 img
Parkisons and Decorrelation
1 Data Decorrelated Options Here I’ll show the impact of decorrelating high-dimensional data sets. library("FRESA.CAD") library(whitening) library(mRMRe) library(readxl) 1.1 ML Method with scaling and decorrelation decorrelated_ML_Method <- function(theformula, data = NULL, mlMet...
7288 sym R (9737 sym/24 pcs) 7 img 17 tbl
ARCENE decorration and Two Set Cross-Validation
1 Data Decorrelated Options Here I’ll show the impact of decorrelating high-dimensional data sets. library("FRESA.CAD") library(whitening) 1.1 The ARCENE Data Set trainLabeled <- read.delim("./trainSet.txt") validLabeled <- read.delim("./arcene_valid.txt") trainLabeled$Labels <- 1*(trainLabeled$Labels > 0) validLabeled$Labels <- 1*(val...
1868 sym R (9175 sym/30 pcs) 4 img 14 tbl
FRESA.CAD Feature Decorrelation (pseudo whitening)
1 Data Decorrelated Options Here I’ll show the impact of decorrelating high-dimensional data sets. library("FRESA.CAD") library(whitening) 1.1 The ARCENE Data Set trainLabeled <- read.delim("./trainSet.txt") validLabeled <- read.delim("./arcene_valid.txt") trainLabeled$Labels <- 1*(trainLabeled$Labels > 0) validLabeled$Labels <- 1*(val...
538 sym R (11984 sym/27 pcs) 17 img 2 tbl
Feature decorrelation ARCENE
1 Filters on Raw and Decorrelated data Here I’ll show the impact of decorrelating high-dimensional data sets. library("FRESA.CAD") 1.1 The ARCENE Data Set trainLabeled <- read.delim("./trainSet.txt") validLabeled <- read.delim("./arcene_valid.txt") trainLabeled$Labels <- 1*(trainLabeled$Labels > 0) validLabeled$Labels <- 1*(validLabeled...
407 sym R (24894 sym/17 pcs) 12 img
COVID-19 Mexico Adultos Mayores
Covid19 En México Este documento muestra una estimación del número de hospitalizados y de muertes debido a COVID-19. Los datos son extraídos de los Datos Abiertos - Dirección General de Epidemiología de la secretaria de salud de México. https://www.gob.mx/salud/documentos/datos-abiertos-152127 Los datos del archivo .csv son importados a R ...
4040 sym R (14157 sym/14 pcs) 54 img