# Fuzzy Case-Based Reasoning System
## 4 Experiments and Discussion
### Changes in Prediction with Changes in Fuzzy Classification  
This section is meant to specify that the increment of prediction accuracy may or may not be achieved by sacrificing the prediction precision. Correspondingly, the advantage of this system is that it can increase prediction accuracy without sacrificing precision in an ideal situation, and the disadvantage is that it may sometimes sacrifice precision. From CBR to FCBR(1), there are both  
Figure 5: (**a**) Testing results of case-based reasoning (CBR). (**b**) Testing error of CBR.  
Figure 6: (**a**) Testing results of fuzzy expert system. (**b**) Testing error of fuzzy expert system.  
increment and sacrifice; from FCBR(1) to FCBR(2), there is no increment and no sacrifice; from FCBR(1) to FCBR(2), there is increment and no sacrifice.  
**Analysis:** From the context above, we change [PERSON]'s idea to represent fuzzy sets as \"if-then\" statements. In other words, an interval can be expressed as a fuzzy set. Because of this, different intervals match with different fuzzy set and fuzzification process can be treated as the process of interval merge.  
FCBR starts with the original fuzzy classification and experiment shows results in Table 8 as FCBR(1) and in Figure 7. Then, we change fuzzy classification of atmosphere pressure from Moderate, Slightly Lower, Lower and Lowest into three classes: Moderate, Lower and Lowest. In other words, we merge Moderate and Lower slightly, which show in Table 9. Next, we merge dry and wet bulb temperature Lower and Slightly Lower into Lower according to Table 10. We clearly see that merge Slightly Lower and Lower does not influence the prediction results since Slightly Lower and Lower have the same effects on the prediction results: both of them are sensitive or non-sensitive to the precipitation. However, when we change dry and wet bulb temperature, the hitting rate is increased since one of them is sensitive and the other is non-sensitive and through merging the effect of non-sensitivity is reduced.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Dry and Wet Bulb** & **Lowest** & **Lower** & **Moderate** & **Higher** & **Highest** \\
**Temperature Rating** & & & & & \\ \hline \({}^{\circ}\)C & \(<\)\(-10\) & \([-10,5)\) & \([5,30)\) & \([30,45)\) & \(>\)45 \\ \hline \hline \end{tabular}
\end{table}
Table 10: Fuzzy classification of dry and wet bulb temperature.  
Figure 8: (**a**) Testing results of FCBR(2). (**b**) Testing error of FCBR(2).  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Experimental** & **Minimum** & **Maximum** & **Minimum** & **Maximum** & **Average** & **Hit Rate** \\
**Results** & **Error** & **Error** & **\(|\)Error** & **\(|\)Error** & \(|\)Error** & **Hit Rate** \\ \hline CBR & \(-2.1\) mm & \(-28.1\) mm & 0.094 mm & 28.1 mm & 2.49 mm & 0 \\ FCBR(1) & \(-1\) level & 3 level & 0 level & 3 level & 1.04 level & 0.28 \\ FCBR(2) & \(-1\) level & 3 level & 0 level & 3 level & 1.04 level & 0.28 \\ FCBR(3) & \(-3\) level & 2 level & 0 level & 3 level & 0.57 level & 0.57 \\ Fuzzy expert system & 26.9 mm & 125 mm & 26.9 mm & 125 mm & 54.6 mm & 0 \\ \hline \end{tabular}
\end{table}
Table 8: Comparison between case-based reasoning (CBR) and fuzzy case-based reasoning system.  
Figure 9: (**a**) Testing results of FCBR(3). (**b**) Testing error of FCBR(3).