Enhanced binary genetic algorithm as a feature selection to predict student performance

Expecting individuals’ body reaction to Covid-19 based on statistical Naïve Bayes technique

“…In this subsection, the IBGA that consists of F score method as a fast method and BGA as an accurate method will be evaluated and compared to many of the modern feature selection methods and also compared to the original dataset without feature selection (Original). The modern selection methods used in the comparison are Genetic Algorithm (GA) [ 2 , 27 , 28 ], Feature Selection via Directional Outliers Correcting (FSDOC) [36] , Orthogonal Least Squares (OLS) based feature selection method [37] , the Modified Grasshopper Optimization Algorithm (MGOA) [38] , and Stochastic Diffusion Search (SDS) algorithm [29] . To evaluate these features selection methods, NB classifier is used as a standard method [ 29 , 30 , 31 ].…”

“…According to figure 7 , after implementing F Score , IBGA begins with a population that contains many chromosomes in which each chromosome consists of a series of genes in form of bits [ 27 , 28 ]. The length of each chromosome is the number of features in dataset with binary values (0 or 1) where ‘1’ in the j th position in the chromosome means that the j th feature is selected and ‘0’ means that the j th feature is removed.…”

“…Despite its simplicity, NB classifier is one of the powerful classification techniques. Due to its easiness, along with its good performance, NB is widely used to address classification problems in several real-world applications [ 28 , 32 ]. NB has been chosen to be implemented in CP for the following reasons; (i) it can provide fast predictions rather than other classification algorithms because the training time has an order O(N) with the dataset, (ii) NB can be easily trained with small amount of input training dataset and it can be used also for large datasets as well, (iii) NB is easy to be implemented with the ability of real-time training for new items, (iv) it has no required adjusting parameter or domain knowledge, (v) NB is less sensitive to missing data, (vi) it has high capability to handle the noise in the dataset, (vii) NB is Incremental learning algorithm because NB functions work from approximation of low-order probabilities which are extracted from the training data [ 33 , 34 , 35 ].…”

Enterprise Financial Early Warning Based on Improved Whale Optimization Algorithm: Optimize the Perspective with Indicators

“…When preferential dimensionality reduction is performed based on the correlation information of indicators, features that are favorable to the identification results may be excluded, and the interpretability of the model is relatively poor when the financial early warning model is constructed using factor analysis. Feature preference dimensionality reduction using metaheuristic algorithms has been widely used in areas such as behavioral recognition [10], network intrusion detection [11], and performance prediction [12]. For the field of financial early warning, metaheuristic algorithms are mostly used for hyperparameter optimization of classifiers [13][14][15] and are less frequently applied in financial indicator preference dimensionality reduction.…”

Investigation on the Identity Construction of Young Foreign Language Teachers in Colleges and Universities Based on Feature Selection Algorithm

“…Teachers' arrangements for classroom content are diverse, as well as teachers' introduction to foreign cultures. Among the three teachers involved in the questionnaire, Teacher B has the shortest teaching age, so he has the least emotional training for students, and Teacher C has participated in teaching for 15 years, so he has the most profound emotional concern for students [15]. e mean value also shows that the three teachers of different teaching ages attach great importance to the grasp of students' emotional orientation.…”