Machine Vision Inspection Systems, Machine Learning-Based Approaches. Группа авторов
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Название: Machine Vision Inspection Systems, Machine Learning-Based Approaches

Автор: Группа авторов

Издательство: John Wiley & Sons Limited

Жанр: Программы

Серия:

isbn: 9781119786108

isbn:

СКАЧАТЬ result by applying kNN technique.

      Case-III (NoF = 5)

Schematic illustration of the classification result by applying LR technique for case three (NoF equals 5). Schematic illustration of the classification result by applying NN technique for case three (NoF equals 5).

      Figure 1.16 Classification result by applying NN technique.

      Figure 1.17 Classification result by applying kNN technique.

Schematic illustration of the classification result by applying NB technique for case three (NoF equals 5).

      Case-IV (NoF = 10)

Schematic illustration of the classification result by applying LR technique for case four (NoF equals 10).

      Figure 1.20 Classification result by applying NN technique.

Schematic illustration of the classification result by applying kNN technique for case four (NoF equals 10).

      Figure 1.21 Classification result by applying kNN technique.

Schematic illustration of the classification result by applying NB technique for case four (NoF equals 10).

      Case-V (NoF = 20)

Schematic illustration of the classification result by applying LR technique for case five (NoF equals 20). Schematic illustration of the classification result by applying NN technique for case five (NoF equals 20).

      Figure 1.24 Classification result by applying NN technique.

Schematic illustration of the classification result by applying kNN technique for case five (NoF equals 20).

       Figure 1.25 Classification result by applying kNN 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.

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

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