## 4 Experimental Result Analysis  
For the experiment of this research, initially, we have collected pen-tablet handwriting samples from 25 different persons of age 19-40. Users in the experiment were given 10 different keywords (Japan, Machine Learning, Tokyo, Hello World, Fukushima, Aizu University, Basic Research, Computer Vision, Pattern Processing and Thank You) to write using the pen-tablet devices. After data collection, for labeling the training and testing dataset, we have separated our dataset into two portions. A total 60% of the dataset is separated for training the system and 40% of the dataset for testing purpose. In our research, during data collection, each key word is written 5 times by each person. Thus from (10 key words \(\times\) 5 times) = 50 data samples of one person, total (10 \(\times\) 3) = 30 data samples are randomly selected for feature selection and training process and rest of the (10 \(\times\) 2) = 20 data samples are selected for testing process. The same procedure is repeated for the 25 classes. Thus a total 1250 writing samples and 25 classes are used in the experiment. After data collection, the test dataset and train dataset kept separated. The test data is not mixed up with the training data. Table 4 presents the summary of dataset labeling. All the experiments are implemented on a laptop computer Intel Core i5 (2.20 GHz) and 8 GB RAM with operating system windows (\(\times\)64) version 10, using Python programming language, Anaconda software, and Wacom Tablet Device. In our research, we utilized 3 different machine learning algorithms (SVM, LR, and RF) to show the stability of our proposed model by measuring the classification results. There are two phases of our experiment: (1) Classification without feature selection (all features) and (2) Classification with feature selection (selected features). To evaluate the performance of the proposed model, four evaluation metrics are used for which we need to compute the parameters of True Positive (TP), False Positive (FP), True Negative (TN), and False Negative (FN). A confusion matrix is generated by these four evaluation metrics of classification results. These four evaluation metrics are computed as follows:  
\[Accuracy\ =\ \frac{T_{p}+T_{n}}{T_{p}+F_{p}+F_{n}+T_{n}} \tag{9}\]  
\[Precision\ (P)\ =\frac{T_{p}}{T_{p}+F_{p}} \tag{10}\]  
\[Recall\ (R)\ =\frac{T_{p}}{T_{p}+F_{n}} \tag{11}\]  
\[F_{1}Score=\frac{2\times\ (P\times R)}{P+R} \tag{12}\]  
The writing samples of different users are not the same. Hence, there is visible discrimination between the same feature values of different persons. Figure 9 shows different features of writing samples for different persons. There are 30 main features in the experiment (f\({}_{1}\)-f\({}_{30}\)). These feature vectors are directly applied to the filter approach for optimal feature selection. The filter technique is executed by two different SFFS algorithms on the two randomly splitted datasets containing equal numbers of data. Individual SFFS algorithm generates separate optimal feature sets from both datasets. This whole process is repeated for 4 iterations. Finally, the filter technique generates 10 sub-optimal feature sets from the 30 datasets. Then the 10 sub-optimal feature sets are forwarded to the wrapper approach, where the classification accuracies are measured using SVM, LR, and RF for each of the sub-optimal feature vectors. Table 5 shows the 10 sub-optimal feature sets after the filter approach. In our experiment, firstly, we have done the best optimal feature selection process with SVM and the evaluation is also done with the same classifier that is SVM. The classification accuracy using SVM is about 98% which shows the practicality of our system. However, to prove the stability of our system, we implemented our model with two additional machine learning algorithms such as LR and RF classifier. For that, the feature selection and evaluation processes are done using LR and RF accordingly following the same procedure of SVM.