# Predicting Academic Performance Using an Efficient Model Based on Fusion of Classifiers
## 3 Research Methodology
Information gain is a filter-based feature ranking technique that is based on the information theory where information is provided about the target class attribute given the value of the dependent class attribute [33]. Out of 21 features, 16 were selected based on the rank that is close in its relationship with the final predicted outcome and gives better results. The foremost motive of the proposed model is to predict the students' performances under the classes such as A+, A, B+, B, C, D, and F, where nominal and numeric data are converted into ordinal values with the help of the discretization technique. The class distribution is shown in Table 2.  
The research methodology is mainly premised on ensemble methods including bagging, boosting, and stacking, which is a different kind of ensemble method which uses a blend of models [29]. Among these methods, bagging, boosting, and stacking can be utilized for classification and prediction. Each model has some strengths and limitations, so the ultimate objective of ensemble methods is to complement the models, in order to achieve higher prediction accuracy. The bagging method is used to sort the tuples randomly into different bags while developing a model. The process is known as bootstrap aggregation. All models in bagging are built in parallel and, for the overall decision, an average is taken from all models, which lowers the variance in the model. It has been shown that bagging achieves the highest efficiency relative to the other methods [34; 35]. Bootstrap improves on bagging. It is developed sequentially by assigning weights to the tuples classified incorrectly by previous classifiers and thus receive more attention \begin{table}
\begin{tabular}{c c c} \hline \hline
**S. No** & **Attributes** & **Description** \\ \hline
1 & GE & Gender (Male, Female) \\ \hline
2 & HA & Home Address Urban, Rural) \\ \hline
3 & PCA & Parent Cohabitation Status (Living together, Apart) \\ \hline
4 & QFR & Quality of family Relationship (Very Good, Good, Not Good) \\ \hline
5 & MJ & Mother Job (Yes, No) \\ \hline
6 & FJ & Father Job (Yes, No) \\ \hline
7 & ME & Mother Education (None, Elementary, Secondary, Higher) \\ \hline
8 & FE & Father Education (None, Elementary, Secondary, Higher) \\ \hline
9 & FS & Family Size (Less than 3, Greater or equal to 3) \\ \hline
10 & GF & Going out with friends (Yes, No) \\ \hline
11 & PF & Past Failures (Yes, No) \\ \hline
12 & NS & Attended Nursery School (Yes, No) \\ \hline
13 & HE & Want to take Higher Education (Yes, No) \\ \hline
14 & R & Relationship (Yes, No) \\ \hline
15 & IA & Internet access at home (Yes, No) \\ \hline
16 & ECA & Extra-Curricular Activities (Yes, No) \\ \hline
17 & DST & Daily Study Time (<2 h, 2 to 5 h, 5 to 10 h, >10 h) \\ \hline
18 & HST & Home to school Travel Time (<15 min, 15 to 30 min, 30 min to 1 h, >1 h) \\ \hline
19 & EG & 8 th Class Grades (A+, A, B+, B, C, D) \\ \hline
20 & NG1 & 9 th Class First Term Grades (A+, A, B+, B, C, D, F) \\ \hline
21 & NG2 & 9 th Class Final Term Grades (A+, A, B+, B, C, D, F) \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Dataset description and possible values. from the next classifier. The weighted average is finally taken to build the final decision. The boosting algorithms are highly capable, take weak and low-performing models, and convert them into robust models. The boosting classifier methods include AdaBoost, GradientBoost and MultiBoost, etc. [36]. The effectiveness of the boosting method has been examined and it was found that the method is an efficient and effective strategy for classification and prediction [37; 38]. Random forest is also an ensemble method which is an improved version of bagging, and it is used for classification and regression. In the training phase, it creates multiple decision trees and produces the mode of classes as well as generating a mean prediction of an individual in regression problems. It also performs random sampling on features with the help of feature engineering.