## 3 The Proposed Method
### Dpga
#### 3.1.1 Chromosome Representation
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Field** & **Description** & **Range of Values** \\ \hline \multirow{3}{*}{FFE algo.} & A list of applied filtering-based feature extraction algorithms (Available only for time-series datasets) & A subset of [SMA, CMA, EMA, ES, LFS] (Note that the empty set means that no filtering-based feature extraction is applied.) \\ \cline{3-4}  & & In case of SMA and CMA: \\  & & The number of time points \(n\in\) [3–10] \\  & Parameter values of the applied filtering-based feature extraction algorithms & In case of LFS: \\  & & feature extraction algorithms & Top \(\chi\)\% high-frequency components of the Fourier transform are removed, \(\chi\in\) [10–50] \\  & & For EMA and ES: \\  & & None \\ \hline \multirow{3}{*}{RFE algo.} & A list of applied reduction-based feature extraction algorithms & A subset of [PCA,LSA,FAG,GRP] (Note that the empty set means that no reduction-based feature extraction is applied.) \\ \cline{3-4}  & & In case of PCA, LSA, GRP and SRP: \\ \cline{3-4}  & Parameter values of the applied reduction-based feature extraction algorithms & The number of principal components \(n_{\text{PC}}\in\) [\(1-max\left(\frac{\theta}{0},2\right)\)] \\ \cline{3-4}  & The number of remaining features after merging \(n_{\text{FAG}}\in\) [\(2-max\left(\frac{\theta}{0},3\right)\)] \\ \hline \multirow{3}{*}{PCA flag} & Indicator of whether or not the whole set of features is reduced by PCA & True or False \\ \cline{2-4}  & A parameter value to determine the number of dimensions reduced by PCA & The threshold amount of explained variance \(\epsilon\in\) [90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100] \\ \hline \multirow{3}{*}{LM para.} & & In case of MLP: \\  & & The number of hidden neurons \(n_{\text{HIN}}\in\) [5–20] \\  & & The type of activation function \(n_{\text{HIN}}\in\) [\(\left\lceil\text{s},\text{tanh}\right\rceil\)] \\  & & The type of optimization method \(w_{\text{HIN}}\in\) [\(\left\lceil\text{s},\text{tanh}\right\rceil\)] \\  & Parameter values used for a specified learning algorithm & In case of kNN: \\  & & The number of neighbors \(w_{\text{NN}}\in\) [\(2\)–10] \\  & & The type of weight function \(w_{\text{FNN}}\in\) [\(\left\lfloor\text{uniform},\text{distance}\right\rceil\)] \\  & & In case of SVM: \\  & & The penalty parameter C \(\in\) [1–8] \\  & & In case of RF: \\  & & The number of trees \(n_{\text{true}}\in\) [2–10] \\ \hline \hline \end{tabular}
\end{table} 
Table 2: _Cont._account for more than \(\epsilon\%\) of the data variability are selected as the final input variables to be fed into a learning method. * Learning method: As explained before, we employed four machine-learning algorithms in this study. Therefore, the 'LM para.' field represents the corresponding parameters that are necessary to run the learning method as follows:
* MLP: The MLP of a single hidden layer is assumed and \(n_{HN}\) denotes the number of hidden nodes. In addition, the type of the activation function (\(actf_{HL}\)) is selected between the hyperbolic tan function (\"tanh\") and the rectified linear unit function (\"relu\"). The solver for weight optimization (\(sv_{WO}\)) is also selected between an optimizer in the family of quasi-Newton methods (\"bfgs\") [51] and a stochastic gradient-based optimizer (\"adam\") [52]. * kNN: \(n_{NN}\) denotes the number of nearest neighbors. In addition, the weight function (\(wf_{NN}\)) is selected between \"uniform\" and \"distance.\" In the former, the neighbors are weighted equally, whereas the neighbors are weighted by the inverse of the distance to the query in the latter. * SVM: C denotes the penalty parameter for the misclassification. * RF: \(n_{tree}\) denotes the number of trees in the forest.