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simeonem

Simeone Marino

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DataSifter_R1
Binomial_dataset_3_final
Binomial_dataset_1_final
Binomial_dataset_final
Null_Dataset_1_final
Binomial 300-300 Amplitude 10
Binomial 300-900 Amplitude 10
ADNI_MN3_top50_exp1
ADNI MN3 BALANCED
Binomial_dataset_Accuracy
The ranking is done here by accuracy of each single prediction
Binomial_dataset_MSE
The ranking is done here by MSE
Data Sifter v8
Null_Dataset
Null_Dataset_1
Null_Dataset_3
Null_Dataset_5
300-100 Exp1 Binomial
300-300 Exp1 Binomial
300-900 Exp1 Binomial
Data Sifter v4
DataSifter - plotly
Data Sifter v0
ADNI MN3 only
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.