# Automatic Literature Mapping Selection: Classification of Papers on Industry Productivity
## 3 Results
### Category Classification  
The assessment encompasses the Multinomial Naive Bayes (MNB), SGD Classifier, Support Vector Machine (SVM), and Decision Tree models. Each of these models underwent multiple runs with different hyperparameter configurations, and their effectiveness was gauged by metrics such as the F-measure [19; 20]. The primary objective is to discern the most fitting model for the given challenge of subject classification. This scrutiny involved processed data transformed into vectors. The dataset is composed of labeled documents spanning diverse subject categories. The task involves training the models to aptly classify new documents into their appropriate categories. Table 2 shows the values of the F-measure metric for each model. This is the most suitable metric when we have an unbalanced database. We highlight the remarkable rate for this metric for the Decision Tree model, which reached a percentage of over 74%. \begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Variables**} & \multicolumn{2}{c}{**MultinomialNB** **(Naive Bayes)**} & \multirow{2}{*}{**SGDClassifier**} & \multirow{2}{*}{**SVM**} & \multirow{2}{*}{**Decision Tree**} \\  & & & & \\ \hline F-measure & 0.6606 & 0.7134 & 0.7367 & 0.7441 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Web of Science results table. in our subject classification endeavor (Figure 6), we observed that the F-measure rests at 66.06%. Despite the advantages of its simplicity and computational efficiency, the outcomes suggest that MNB might not be the optimal choice for our specific subject classification task. In this context, exploring alternative models that have demonstrated more promising outcomes becomes pivotal. This exploration aims to pinpoint the model that best aligns with the project's requirements and objectives. The Stochastic Gradient Descent (SGD). The SGD classifier, a linear classifier employing stochastic gradient descent for optimization, was subjected to performance evaluation in our subject classification undertaking. Our findings demonstrate that the SGD classifier attained the F-measure of 71.34%. This outcome underscores the model's superiority over MNB, and this improvement can be attributed to its enhanced capability to tackle more intricate classification challenges (Figure 7). The enhanced F-measure demonstrated by the SGD classifier, compared to MNB, can be attributed to its inherent characteristics and proficiency in handling linear relationships among problem features. This ability proves especially valuable when dealing with intricate classification challenges. Consequently, these outcomes suggest that the SGD classifier presents a more favorable prospect for the subject classification task than MNB. However, it remains crucial to thoroughly consider the other evaluated models to ascertain the most suitable option that aligns with the precise demands of this study. Support Vector Machines (SVM). Recognized for its efficacy in high-dimensional spaces, the Support Vector Machine (SVM) stands as a frequently employed classifier in classification tasks. Our findings reveal that the SVM garnered an F-measure metric of 73.67%. These demonstrate that the SVM surpassed the performance of both MNB and SGD classifiers. This highlights many true positives, evident within the associated confusion matrix (Figure 8). The remarkable outcomes observed in accuracy, recall, and precision, when contrasted with those of the MNB and the SGD Classifier, strongly validate the SVM's potency for subject classification tasks. Notably, it shines in scenarios characterized by expansive data dimensionality and intricate classification intricacies. Figure 6: Confusion Matrix (Naive Bayes). Consequently, these findings underscore the SVM's stature as a remarkably competitive and accomplished choice for the subject classification endeavor. Nevertheless, it remains imperative to weigh additional factors like runtime and interpretability when choosing the most fitting model that aligns with our project's unique prerequisites and objectives.