result by applying kNN technique.
Figure 1.14 Classification result by applying NB technique.
Case-III (NoF = 5)
Figure 1.15 Classification result by applying LR technique.
Figure 1.16 Classification result by applying NN technique.
Figure 1.17 Classification result by applying kNN technique.
Figure 1.18 Classification result by applying NB technique.
Case-IV (NoF = 10)
Figure 1.19 Classification result by applying LR technique.
Figure 1.20 Classification result by applying NN technique.
Figure 1.21 Classification result by applying kNN technique.
Figure 1.22 Classification result by applying NB technique.
Case-V (NoF = 20)
Figure 1.23 Classification result by applying LR technique.
Figure 1.24 Classification result by applying NN technique.
Figure 1.25 Classification result by applying kNN technique.
Figure 1.26 Classification result by applying NB technique.
From the analysis of Figures 1.7–1.26 and Table 1.1, it is observed that NB technique provides better classification results with CA values 0.667, 0.733 and 0.767 as compared to LR, NN and kNN when the NoF is 2, 10 and 20 respectively. Again, NB and kNN techniques provide better classification results with CA value 0.633 as compared to LR and NN technique when the NoF is 3. Similarly, NB and NN techniques provide better classification results with CA value 0.667 as compared to other techniques when the NoF is 5. Here, the maximum CA value is 0.767 which is provided by NB technique when the NoF is 20. So, CA value varies for each technique when different NoF are considered. However, NB technique attempts to provide better classification results in three different cases such as cases-I, -IV, and -V. In case-II, NB and kNN provide same CA values as 0.633 and in case-III, NB and NN provide same CA values as 0.667. However, the number of instances present in different cells of confusion matrix varies for the classification results of NB and kNN in case-II as well as NB and NN in case-III. So, NB technique is able to provide better classification results overall as compared to other technique.
Table 1.1 CA (in unit) of different classification techniques.
| Method | NoF = 2 | NoF = 3 | NoF = 5 | NoF = 10 | NoF = 20 |
|---|---|---|---|---|---|
| LR | 0.567 | 0.533 | 0.633 | 0.567 | 0.600 |
| NN | 0.567 | 0.567 | 0.667 | 0.667 | 0.667 |
| kNN | 0.467 | 0.633 | 0.600 | 0.633 | 0.633 |
| NB | 0.667 | 0.633 | 0.667 | 0.733 | 0.767 |
1.5 Conclusion
