# Datasets for Cognitive Load Inference Using Wearable Sensors and Psychological Traits
## 5 Machine Learning Analysis
### Normalization, Feature Selection, and Model Learning  
To analyze the inter-participant and inter-session influence, experiments were performed without normalization, with session-specific min-max normalization, and with session-specific standardization. When min-max normalization is used, each feature is scaled between 0 and 1 by subtracting the minimal value and then by dividing this difference with the difference between \begin{table}
\begin{tabular}{l c c c c c c c} \hline  & **Subjective Diff** & **Level** & **Click per Second** & **Temperature** & **gr** & **hr** \\ \hline subjective diff & 1 & +0.742 (0.47-0.88) & +0.520 (0.14-0.77) & +0.035 (-0.38-0.44) & -0.009 (-0.42-0.4) & +0.062 (-0.36-0.46) \\ level & +0.742 (0.47-0.88) & 1 & +0.598 (0.42-0.81) & +0.025 (-0.44-0.30) & -0.048 (-0.45-0.47) & +0.072 (-0.35-0.47) \\ TLX\_effort & +0.838 (0.61-0.92) & +0.648 (0.32-0.84) & +0.375 (-0.04-0.68) & +0.077 (-0.35-0.47) & +0.005 (-0.38-0.44) & -0.008 (-0.42-0.41) \\ TLX\_mental demand & +0.750 (0.78-0.89) & +0.513 (1.32-0.36) & +0.319 (-0.11-0.46) & +0.075 (-0.35-0.47) & +0.062 (-0.36-0.44) & -0.008 (-0.42-0.41) \\ TLX\_temporal demand & +0.469 (0.35-0.85) & +0.671 (0.36-0.85) & +0.429 (0.6-0.72) & +0.053 (-0.57-0.46) & +0.083 (-0.38-0.44) & +0.015 (-0.32-0.59) \\ TLX\_physical demand & +0.266 (0.15-0.61) & +0.181 (-0.25-0.55) & -0.042 (-0.56-0.38) & +0.059 (-0.78-0.74) & +0.045 (-0.73-0.65) & -0.304 (-0.64-0.12) \\ TLX\_performance & -0.383 (-0.69-0.04) & -0.355 (-0.07) & -0.261 (-0.61-0.17) & -0.132 (-0.52-0.30) & -0.018 (-0.43-0.4) & -0.014 (-0.43-0.2) \\ TLX\_frustration & +0.413 (0.00-0.70) & +0.385 (-0.03-0.69) & +0.114 (-0.29-0.52) & -0.042 (-0.74-(0.08)) & -0.042 (-0.7-0.01) & -0.149 (-0.53-0.28) \\ \hline \end{tabular}
\end{table} 
Table 6: Correlations between the TLX scores and the objective cognitive load measure for the Snake dataset with 95% confidence interval in parentheses. the minimal and the maximal values. When standardization is used, each feature is mean centered by subtracting the mean value and then dividing with the standard deviation. Additionally, experiments were performed with and without feature selection. In general, all feature selection methods can be divided into wrapper methods, ranking methods (also known as filter methods), and a combination of the two. The wrapper methods (e.g., based on ROC metrics [66]) produce better results compared to the ranking methods (e.g., information entropy [67]), but they induce a heavy computation burden. In this study, a ranking method based on mutual information [68] was used because it is very efficient to compute. Mutual information is a measure that estimates the dependency between two random variables. The features were ranked using mutual information values between the features and the class values estimated on the training data, and only the top-ranked 50 features were used to build models. Experiments were performed with the following ML algorithms: Decision Tree [69], RF [70], Naive Bayes [71], KNN [72], Logistic Regression [73], Bagging using Decision Trees [74], Gradient Boosting (AdaBoost), Extreme Gradient Boosting (XGB), and Multilayer perceptron (MLP) [75]. The specific architecture used for the MLP is available online [[https://repo.ijs.si/martingjoreski/cognitive-load/-/blob/master/MLP.png](https://repo.ijs.si/martingjoreski/cognitive-load/-/blob/master/MLP.png)]([https://repo.ijs.si/martingjoreski/cognitive-load/-/blob/master/MLP.png](https://repo.ijs.si/martingjoreski/cognitive-load/-/blob/master/MLP.png)). It contains two hidden layers, one of size 512 and one of size 32 units, and one output unit that uses the sigmoid activation function. These ML algorithms learn one model for each training dataset. The ML approach capable of learning models for several ML datasets (ML tasks) in parallel while using a shared representation is Multi-task learning (MTL) [76]. The idea is to use what was learned from one dataset to help learn other tasks better.