## 4 Algorithm Bias
### Estimators
Starting from the agricultural domain, the authors in [117] compare three algorithms to estimate soil organic carbon (i.e., SOC), namely RF, SVM and Cubist. Based on the results of the experiments, an ensemble method that utilized all three algorithms was proposed, since it reduced the overall bias when modeling SOC, regardless of the different spectral measurement conditions. In [118], the authors compare several algorithms from reinforcement learning, including SWO, VPSS, BCO and TD, for the scheduling problem. They conclude that SWO performed better since it was particularly designed to solve that kind of problem. Regarding biological data used to predict bacterial heterotrophic fluxomics, the authors in [119] test SVM, KNN and DT and conclude that SVM is the most appropriate one in this use case since it achieves the highest accuracy. As for the business domain, the authors in [120] test the MNB and a CLSTM NN for the classification of clients' reviews. Their results showcase that CLSTM NN is more appropriate for this classification task because the MNB produces biased results since it is learning a single class and neglects the other ones because the training data are imbalanced. In a similar approach [121], the authors compare RF, SVM and a DNN for predicting whether a machine in an assembly line will fail, based on its kinematic, dynamic, electrical current, and temperature data. Even though SVM reduced model overfitting and bias, it did not achieve the desired accuracy whilst the DNN not only improved the accuracy but also produced insignificant bias. In [122], where the authors handle time series data of marine systems the authors experiment on a set of algorithms and methods including STL decomposition, ESM, ARIMA, LinearR, KNN, SVM, VAR, DT and ensemble methods that combine the previously mentioned methods. The authors suggest that the ensemble methods should be avoided since they have a higher computational cost. They also argue that for this specific use case, VAR should be used when many missing values are present in the time series, whilst ARIMA should be chosen if there are no missing values. In a computer vision problem [123], where \begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Algorithm (Estimator) Name** & **Abbreviation** & **Description** \\ \hline Multi-Layer Perceptron neural network & MLP & Subtype of a DNN [106] \\ \hline Multinomial Naive Bayes & MNB & Probabilistic classifier that is based on the Bayes’ theorem and focuses on calculation of text data’s distribution [107] \\ \hline Neural Network & NN & Structure that is made of artificial neurons that receive signals from other connected neurons and create an output that can be forwarded to other neurons [108] \\ \hline Random Forest & RF & Fundamental ML algorithm that makes use of the output of multiple decision trees for classification, regression and other tasks [109] \\ \hline Squeaky Wheel Optimization & SWO & An optimization technique based on a greedy algorithm [110] \\ \hline Seasonal and Trend decomposition using Loess & STL & Method used in time series where linear regression models are applied to decompose a time series into separate components [111] \\ \hline Support Vector Machines & SVM & Classification algorithm that finds an optimal line or hyperplane to maximize the distance between each class [112] \\ \hline Temporal Difference (lambda) & TD & Reinforcement learning method that share common characteristics with Monte Carlo method and Dynamic Programming methods [113] \\ \hline Term Frequency Inverse Document Frequency of records & TF-IDF & A measure calculating the relevancy of a word in a series of words or entire corpus [114] \\ \hline Value-Biased Stochastic Search & VPSS & Randomization method that can be used for determining stochastic bias [115] \\ \hline Vector Autoregression & VAR & Model that can be used for multiple time series that influence each other [116] \\ \hline \hline \end{tabular}
\end{table}