## 3 Methodology
### Classification Algorithms  
For the classification process, four widely used classification algorithms were considered for evaluating the efficacy of the FS methods. These are Naive Bayes (NB), Decision Tree (DT), Kernel Logistic Regression (LR), and K Nearest Neighbor (KNN). This is in line with the aim of this study to evaluate the efficacy of FFR and FSS methods, which should be independent of the classification technique. The four classifiers were selected based on different characteristics: NB based on [PERSON] theorem, DT from Tree-based methods, LR from the function-based classification technique and KNN from the instance based learning classifiers. The heterogeneity of the selected classifiers is to investigate how different FS methods perform on different classifiers with different characteristics. Table 4 gives a brief description of these classification algorithms with respect to their classification characteristics and their parameter settings.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Filter Feature Subset Selection Method** & **Search Method** & **Reference** \\ \hline \multirow{6}{*}{Correlation-based Feature Subset Selection (CFS)} & Best First Search (BFS) & [32; 36] \\ \cline{2-3}  & Greedy Stepwise Search (GSS) & [29; 36] \\ \cline{2-3}  & Ant Search (AS) & \\ \cline{2-3}  & Bat Search (BAT) & \\ \cline{2-3}  & Firefly Search (FS) & \\ \cline{2-3}  & Genetic Search (GS) & [32; 36] \\ \cline{2-3}  & PSO Search (PSOS) & \\ \hline \multirow{6}{*}{Consistency Feature Subset Selection (CNS)} & Best First Search (BFS) & [32; 36] \\ \cline{2-3}  & Greedy Stepwise Search (GSS) & [29; 36] \\ \cline{2-3}  & Ant Search (AS) & \\ \cline{2-3}  & Bat Search (BAT) & \\ \cline{2-3}  & Firefly Search (FS) & \\ \cline{2-3}  & Genetic Search (GS) & [32; 36] \\ \cline{2-3}  & PSO Search (PSOS) & \\ \hline \hline \end{tabular}
\end{table}
Table 2: List of Filter Feature Subset Selection (FSS) Methods.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Search Methods** & **Parameter Settings** \\ \hline Best First Search & Direction = Bi-directional \\ \hline \multirow{2}{*}{Greedy Stepwise Search} & Conservative Forward Selection = True; Search Backwards = False; \\  & NumToSelect = log\({}_{2}\) N (N = Number of Features) \\ \hline \multirow{3}{*}{Ant Search} & AccelerateType = Accelerate; Chaotic co-efficient = 0.4 \\  & Chaotic Parameter Type = Chaotic Map: Parameter; \\  & Chaotic Type = logistic map; PopulationSize = 200; Chromosome = 2.0 \\ \hline \multirow{3}{*}{Bat Search} & AccelerateType = Accelerate; Chaotic co-efficient = 0.4; \\  & Chaotic Parameter Type = logistic Map: Parameter; \\  & Chaotic Type = logistic map; PopulationSize = 200; loudness = 0.5 \\ \hline \multirow{3}{*}{Firefly Search} & AccelerateType = Accelerate; Chaotic co-efficient = 0.4; \\  & Chaotic Parameter Type = logistic Map: Parameter; \\  & Chaotic Type = logistic map; PopulationSize = 200; \\  & \(\text{absorption}=0.001\text{; betaMin}=0.33\) \\ \hline Genetic Search & PopulationSize = 200; MaxGeneration = 20; crossoverProb = 0.6 \\ \hline \multirow{2}{*}{PSO Search} & PopulationSize = 200; IndividualWeight = 0.34; InertiaWeight = 0.33; \\  & SocialWeight = 0.33 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Various Search Methods and Parameter Setting.