## 4 Results and Discussion
### Supervised Machine Learning Analysis for Pattern Recognition
This means that MLP can classify only 50% of combustion activity correctly and it cannot classify the other 50% correctly as combustion activity. Table 11 presents the confusion matrix for VAN (before PCA) which has the highest classification accuracy. Compared to the confusion matrix of SN in Table 10, MLP does not have any confusion in classifying all the five conditions. It means that it can classify all of the five conditions correctly. This confusion matrix validates the classification rate for VAN (before PCA), which is 100%. Figure 8: Classification performance for the before- principal component analysis (PCA) and after-principal component analysis (PCA) dataset. The classification accuracy that has been achieved by this study is quite similar to the classification result achieved by a previous researcher [46]. They have developed a laboratory-made maladour sensing system, used to identify five typical sources of olfactive annoyance: printing houses, a paint shop in a coach building, wastewater treatment plant, urban waste composting facilities and a rendering plant. The researcher adopted various data pre-processing techniques, such as the VAN feature, which was also used in this study. Their results also show that the best classification results are obtained using a VAN feature with 100% classification accuracy. The objective of testing these three classifiers is to see which classifier gives the highest classification accuracy. Based on the results of the classifiers discussed before, there are four sets of classifiers with one feature (VAN before PCA) which gave 100% classification accuracy:  
1. MLP-VAN feature before PCA (model 9-3-5),
2. MLP-VAN feature before PCA (model 9-9-5),
3. MLP-VAN feature before PCA (model 9-12-5), and
4. KNN-VAN feature before PCA (K factor is 2). To prove that the VAN feature before PCA really gave 100% classification accuracy, another analysis has been done. The PCA visualization for the VAN feature before any dimensionality reduction is constructed in s 3D plot as shown in Figure 9. From Figure 9, it can be seen that none of the five conditions coincide with each other, and therefore they are mutually exclusive. After the feature has been identified, it is now time to choose between the two classifiers: MLP or KNN. This study chooses MLP with a model structure of 9-3-5 because it is easier to be embedded in the system. Model 9-3-5 only has three hidden variables, while the other two model structures have nine and 12 hidden variables. A model structure with fewer hidden variables has a less complicated formula and is therefore easy to be embedded. As far as KNN is concerned, KNN requires a large storage space in the system because it saves every data that it receives. MLP, on the other hand, does not require a large storage system. Due to these reasons, an MLP classifier is chosen for this study. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{4}{*}{Actual} & \multicolumn{8}{c}{**Predicted**} \\ \cline{2-8}  & **Sources of IAQ** **Pollutant** & **Ambient** & **Chemical** & **Food \& Beverages** & **Fragance** & **Human Activity** & **Confusion Level (\%)** \\ \cline{2-8}  & Ambient & 276 & 88 & 20 & 0 & 0 & 28.13 \\  & Chemical & 16 & 308 & 50 & 10 & 0 & 19.79 \\  & Food \& Beverages & 0 & 16 & 280 & 84 & 4 & 27.08 \\  & Fragrance & 6 & 16 & 40 & 288 & 34 & 25.00 \\  & Human Activity & 4 & 12 & 60 & 116 & 192 & 50.00 \\ \hline \hline \end{tabular}
\end{table} 
Table 10: Confusion matrix of multilayer perceptron (MLP) for sensor normalization (SN) after principal component analysis (PCA).