## 7 Efficient Classifiers Optimization  
In the final stage of the computational modelling shown in Figure 7, we describe the process of classifier optimisation. There are two main focuses in the classifier optimisation, _i.e._, to select the proper parameter of the classifier and to obtain lean but well performing classifier. The second focus is of particular relevance in mobile implementation due to imposed resource limitations. The parameters of classifiers play an important role for obtaining good results. For example, in the nearest neighbor classifier (\(k\)-NN), the sensitivity of \(k\) parameter has been investigated for the gas benchmark data as shown in Figure 24[26]. In automated feature selection, employing a classifier as assessment function (wrapper method), the representation of candidate solutions of GA or PSO is extended by including the chosen classifier's parameters along with the binary feature in the gene or particle during optimisation. Practically, this describes a special case of simultaneously co-evolving two steps of the design process, _i.e._ dimensionality reduction and classification [14]. Many classifiers, for instance, nearest neighbor (\(k\)-NN) and probabilistic neural networks (PNN), use the training set as _prototypes_ to evaluate the new patterns. There are numerous related previous works focused on the designing lean classifiers, _i.e._, particularly resource-aware classifier instances. [PERSON] proposed that pruning methods reduced the amount of data which has to be stored for the nearest neighbor classifier called Condensed Nearest Neighbor (CNN) [55]. [PERSON] proposed a postprocessing \begin{table}
\begin{tabular}{l c c c c c c c c c} \hline \multirow{2}{*}{**Dataset**} & \multirow{2}{*}{\(S_{f}\)} & \multicolumn{2}{c}{**HAF**} & \multicolumn{2}{c}{**ELRF**} & \multicolumn{2}{c}{**NLF**} & \multicolumn{2}{c}{**Class (\%)**} \\ \cline{3-10}  & & **Class. (\%)** & **Feature** & **Class. (\%)** & **Feature** & **Class. (\%)** & **Feature** & **Mean** & **Median** \\ \hline Wine & 0.57 & 96.06 & 4 & 96.62 & 6 & 96.06 & 4 & 95.41 & 96.06 \\ Rock & 0.59 & 99.09 & 3 & 95.45 & 6 & 99.09 & 3 & 97.65 & 98.18 \\ Eye image & 0.09 & 96.24 & 7 & 99.24 & 22 & 97.74 & 17 & 96.77 & 96.99 \\ \hline \end{tabular}
\end{table} 
Table 10: Results of selected features by HAF, ELRF, and NLF methods using \(k\)-NN voting classifier with \(k=5\). Figure 24: Sensitivity investigation of \(k\)-NN using extracted features by Gaussian kernel (GWF) of gas benchmark data. The \(k\) parameter values are set as 1, 3, 5, 7, and 9.  
step for the CNN pruning algorithm called Reduced Nearest Neighbor (RNN) [54]. The Restricted Coulomb Energy (RCE) is a three layer feedforward network, which gradually selects the prototypes in a only growing approach and adjusts their radii until satisfactory training. The limitations of CNN, RNN, and RCE methods are: (1) their result strongly depends on the presentation order of the training set and (2) prototypes are selected from training without any adjustment. The work of [PERSON] introduced Resource-Allocating Networks (RAN), which are related to RCE and Radial Basis Function networks (RBF) [56]. The RAN method allows to insert new prototypes for of Gaussian kernels, which will be adjusted as well as their centers by gradient descent technique in training. This attractive method is hampered due to well-known gradient descent limitations. Improvements can be found, e.g., in [PERSON] _et al_. [39], where a new algorithm for nearest neighbor classification called Adaptive Michigan PSO, which can obtain less number of prototypes and is able to adjust their positions is proposed. These adjusted prototypes using AMPSO can classify the new patterns better than CNN, RNN, and RCE. The manner of encoding of the Michigan PSO is much better suited to optimize the named classifiers' structure than the standard PSO. Thus, the Michigan approach was chosen for our work.