| Data Set |
2D |
5D |
| Participant |
Algorithm |
Train |
Dev |
Eval |
Train |
Dev |
Eval |
|
SPRING 2020 |
|
Ahmadzadeh, Omidreza (2020 Spring)
|
MATLAB: K Nearest Neighbors (KNN) |
07.81% |
07.85% |
08.05% |
36.71% |
36.70% |
37.01% |
|
Ahmadzadeh, Omidreza (2020 Spring)
|
Keras: Multilayer Perceptron (MLP) |
08.13% |
07.85% |
08.95% |
36.40% |
37.11% |
36.58% |
|
Canete Riaza, Santiago (2020 Spring)
|
Scikit: Logistic Regression (LGR)
+ K-MEANS + Bagging |
08.04% |
07.95% |
07.70% |
36.71% |
36.66% |
36.66% |
|
Canete Riaza, Santiago (2020 Spring)
|
Keras: Artificial Neural Network (ANN) |
07.90% |
07.85% |
07.95% |
36.31% |
36.65% |
36.64% |
|
Derakhshan, Mohsen (2020 Spring)
|
MATLAB: K Nearest Neighbors (KNN) |
07.86% |
07.35% |
07.85% |
35.73% |
35.47% |
37.55% |
|
Derakhshan, Mohsen (2020 Spring)
|
MATLAB: Multilayer Perceptron (MLP) |
07.91% |
08.40% |
08.10% |
36.63% |
36.92% |
37.25% |
|
Ryskalczyk, David (2020 Spring)
|
Scikit: K Nearest Neighbors (KNN) |
07.69% |
07.80% |
08.15% |
36.84% |
37.33% |
36.87% |
|
Ryskalczyk, David (2020 Spring)
|
PyTorch: Multilayer Perceptron (MLP) |
08.30% |
08.50% |
08.35% |
38.24% |
37.78% |
38.38% |
|
Xin, Pujie (2020 Spring)
|
Python: K Nearest Neighbors (KNN) |
07.91% |
08.30% |
08.00% |
34.72% |
38.47% |
38.12% |
|
Xin, Pujie (2020 Spring)
|
PyTorch: Multilayer Perceptron (MLP) |
08.87% |
09.20% |
08.50% |
37.82% |
38.83% |
39.81% |
|
Carvalho, Pedro (2020 Spring)
|
MATLAB: Support Vector Machines (SVM) |
07.76% |
07.90% |
07.80% |
36.61% |
36.47% |
36.59% |
|
Carvalho, Pedro (2020 Spring)
|
Scikit: Multilayer Perceptron (MLP) |
08.00% |
07.95% |
07.75% |
36.52% |
36.50% |
36.97% |
|
Medeiros, Rodrigo (2020 Spring)
|
Scikit: Random Forests (RNF) |
05.44% |
08.40% |
08.65% |
33.28% |
36.83% |
37.00% |
|
Medeiros, Rodrigo (2020 Spring)
|
PyTorch: Multilayer Perceptron (MLP) |
08.14% |
08.30% |
08.05% |
36.22% |
36.87% |
36.82% |
|
Palma, Rodrigo Demetrio (2020 Spring)
|
Scikit: K Nearest Neighbors (KNN) |
07.93% |
07.95% |
07.85% |
36.88% |
37.07% |
36.81% |
|
Palma, Rodrigo Demetrio (2020 Spring)
|
TensorFlow: Multilayer Perceptron (MLP) |
07.71% |
07.95% |
08.35% |
36.54% |
36.55% |
36.48% |
|
FALL 2019 |
|
Elseify, Tarek (Baseline - Fall 2019))
|
PyTorch: Multilayer Perceptrin (MLP) |
09.34% |
10.05% |
09.15% |
37.47% |
37.14% |
37.42% |
|
Elseify, Tarek (Baseline - Fall 2019))
|
TensorFlow: Multilayer Perceptron (MLP) |
08.63% |
09.10% |
09.00% |
36.89% |
37.30% |
36.87% |
|
Bala, Animesh (Fall 2019))
|
Scikit: Random Forests (RNF) |
06.86% |
08.90% |
08.50% |
30.27% |
36.88% |
36.48% |
|
Bala, Animesh (Fall 2019))
|
PyTorch: Multilayer Perceptron (MLP) |
08.07% |
07.90% |
08.40% |
39.91% |
39.93% |
39.94% |
|
Begaj, Brandon (Fall 2019))
|
Python: Support Vector Machines (SVM) |
08.11% |
07.90% |
08.35% |
36.56% |
36.35% |
36.68% |
|
Begaj, Brandon (Fall 2019))
|
PyTorch: Multilayer Perceptron (MLP) |
09.02% |
09.70% |
09.05% |
36.80% |
36.79% |
36.85% |
|
Bruno, Casey (Fall 2019))
|
Scikit: Random Forests (RNF) |
07.22% |
08.55% |
08.35% |
24.62% |
36.96% |
36.60% |
|
Bruno, Casey (Fall 2019))
|
PyTorch: Extreme Learning Machine (ELM) |
08.22% |
08.50% |
08.40% |
36.47% |
38.48% |
38.32% |
|
Campbell, Christopher (Fall 2019))
|
Python: K Nearest Neighbors (KNN) |
07.69% |
07.85% |
08.50% |
38.49% |
38.93% |
39.16% |
|
Campbell, Christopher (Fall 2019))
|
TensorFlow: Generative Adversarial Networks (GAN) |
09.57% |
08.80% |
09.25% |
40.11% |
40.20% |
40.07% |
|
Jiang, Kuang (Fall 2019))
|
Scikit: Random Forests (RNF) |
06.66% |
06.40% |
08.30% |
13.72% |
37.42% |
37.65% |
|
Jiang, Kuang (Fall 2019))
|
PyTorch: Multilayer Perceptron (MLP) |
07.83% |
07.50% |
08.15% |
36.43% |
36.19% |
37.11% |
|
Khalkhali, Vahid (Fall 2019))
|
Scikit: Gaussian Mixture Models (GMM) |
07.97% |
07.65% |
08.05% |
37.23% |
36.80% |
37.13% |
|
Khalkhali, Vahid (Fall 2019))
|
PyTorch: Multilayer Perceptron (MLP) |
08.07% |
08.15% |
08.30% |
36.31% |
36.60% |
36.96% |
|
Mills, Kenneth (Fall 2019))
|
MATLAB: Support Vector Machines (SVM) |
07.92% |
08.15% |
08.45% |
36.92% |
36.72% |
37.13% |
|
Mills, Kenneth (Fall 2019))
|
MATLAB: Deep Neural Network (DNN) |
17.61% |
16.75% |
17.45% |
41.68% |
41.58% |
41.91% |
|
Pale, Andrew (Fall 2019))
|
MATLAB: K Nearest Neighbors (KNN) |
07.93% |
07.90% |
08.05% |
34.42% |
38.24% |
38.31% |
|
Pale, Andrew (Fall 2019))
|
MATLAB: Multilayer Perceptron (MLP) |
08.35% |
08.00% |
07.75% |
36.96% |
37.33% |
37.81% |
|
Xiao, Ya (Fall 2019))
|
Scikit: Random Forests (RNF) |
00.00% |
08.25% |
09.10% |
00.00% |
38.38% |
38.54% |
|
Xiao, Ya (Fall 2019))
|
PyTorch: Multilayer Perceptron (MLP) |
08.09% |
08.25% |
08.30% |
40.24% |
40.36% |
40.25% |
|
Xie, Zhanteng (Fall 2019))
|
MATLAB: K Nearest Neighbors (KNN) |
08.03% |
08.10% |
08.05% |
36.89% |
37.08% |
36.71% |
|
Xie, Zhanteng (Fall 2019))
|
PyTorch: Multilayer Perceptron (MLP) |
08.22% |
08.05% |
08.10% |
37.22% |
36.97% |
37.26% |
|
Zhou, Tongdi (Fall 2019))
|
MATLAB: Gaussian Mixture Modeling (GMM) |
08.00% |
07.80% |
08.20% |
36.84% |
36.00% |
36.30% |
|
Zhou, Tongdi (Fall 2019))
|
MATLAB: Recurrent Neural Network (RNN) |
08.09% |
08.25% |
07.80% |
36.78% |
36.79% |
36.74% |
|
Zlotnikov, Sivan (Fall 2019))
|
MATLAB: Kernel Linear Discriminant Analysis (LDA) |
09.24% |
09.45% |
09.50% |
38.04% |
37.63% |
38.13% |
|
Zlotnikov, Sivan (Fall 2019))
|
MATLAB: Multilayer Perceptron (MLP) |
08.54% |
09.25% |
08.90% |
39.99% |
39.75% |
40.45% |
|
Campbell, Chris (Fall 2019)
|
Python: Majority Vote (MAJ) |
07.62% |
08.05% |
07.80% |
33.74% |
36.57% |
36.57% |