## Recently Published

##### Binomial_dataset_Accuracy

The ranking is done here by accuracy of each single prediction

##### Binomial_dataset_MSE

The ranking is done here by MSE

##### ABIDE classification (5 Experiments)

Two categories

##### ADNI classification 2 stages (MN4 & Binomial, 4 experiments)

These are the results of classifying the ADNI patients as either Normal, LMCI, MCI or AD (4 categories). The features selected at the end of the analysis of the 4 Exps are then merged to the features selected at the end of the analysis of 9 experiments previously analyzed with a binomial set (e.g., AD vs Normal only). This 2 stages approach improves the AD class sensitivity, which is missed by using only a 1 stage approach with multinomial classification (see http://rpubs.com/simeonem/ADNI_MN4).

##### ADNI classification 2 stages (MN3 & Binomial, 6 experiments)

These are the results of classifying the ADNI patients as either Normal, MCI or AD. The groups MCI and LMCI are merged and labeled MCI (multinomial with 3 categories). The features selected at the end of the analysis of the Exp 1 are then merged to the features selected at the end of the analysis of 9 experiments previously analyzed with a binomial set (e.g., AD vs Normal only). This 2 stages approach greatly improves the AD class sensitivity, which is missed by using only a 1 stage approach with multinomial classification (see http://rpubs.com/simeonem/ADNI_MN3_Exp1-3-7-9).

##### ADNI MN4 + Binomial

ADNI dataset classification results only on Experiment 1.
The methodology here is to:
[STAGE 1] run the full CBDA-SL (i.e., 9000 jobs) on Exp 1
[STAGE 2] run a single CBDA-SL job merging the features selected by STAGE 1 with the binomial stage Normal vs AD (i.e., 9000x9 total jobs, see http://rpubs.com/simeonem/ADNI_Confusion_Matrix for details).

##### ADNI classification 1 stage - MN4

Multinomial classification with CBDA-SL with 4 categories on experiment 1

##### ADNI classification 2 stages

These are the results of classifying the ADNI patients as either Normal, MCI or AD. The groups MCI and LMCI are merged and labeled MCI (multinomial with 3 categories). The features selected at the end of the analysis of the Exp 1 are then merged to the features selected at the end of the analysis of 9 experiments previously analyzed with a binomial set (e.g., AD vs Normal only). This 2 stages approach greatly improves the AD class sensitivity, which is missed by using only a 1 stage approach with multinomial classification (see http://rpubs.com/simeonem/ADNI_MN3_exp1).

##### ADNI dataset - Multinomial Classification on Exp 1

This set of results is based only on Experiment 1. The strategy here is to merge the groups "LMCI" and "MCI" into one, to make it a multinomial with 3 categories (labeled as MN3).

##### ADNI dataset results

CBDA-SL and Knockoff filter results over 9 experiments. No Missing value input has been used (this is because it is a real dataset with some NA that will be filled by the missForest imputation algorithm.
The other specs are: i) SSR of 40-60%, 60-80% and 100%, ii) FSR of 5-15%, 15-30% and 30-50%. These specs make a total of 9 different experiments.

##### NULL dataset - 9000 jobs

Knockoff filter and CBDA-SL results on 12 experiments for a NULL dataset

##### Gaussian dataset - CBDA-SL and Knockoff filter

Results on 30 experiments

##### Histograms CBDA-SL - first set of results

Absolute and relative frequency plots of 5 CBDA-SL experiments, each with 5000 iterations of the SuperLearner (SL) function. Dataset=MRI.
Each plot represents the occurrences of each feature across the top 20 predictions returned by the SL function.
The last 2 plots combine all the results in a single histogram.