An Ensemble Classification Approach Using Improvised Attribute Selection

Memon, Muhammad Qasim; Qu, Shengquan; Lu, Yu; Memon, Aasma; Memon, Abdul Rehman

doi:10.1109/acit53391.2021.9677093

Cited by 1 publication

(6 citation statements)

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“…Firstly, some studies prominently found that IG had significant performance in selecting features out of the dataset [27], [36], [48], [50]. It can be seen that IG had better performance when coupled with some classifiers like DT [27], [36], [50], ANN [36], and RF [48], [50].…”

Section: Discussionmentioning

confidence: 99%

“…The findings underscored the computational expediency of GR, emphasizing its potential as a time-efficient solution for tasks where rapid processing is paramount. Based on the performance of filter-based approaches, GR and Pearson correlation had the highest rank scores (ranging from 0.2 to 1), indicating that these findings were solely influenced by individual characteristics [50]. In the formulation of various data mining techniques for predicting students' performance [29], the GR feature selection method was integrated and paired with seven classifiers.…”

Section: Gain Ratiomentioning

confidence: 99%

“…According to the results, any classification algorithm constructed using heterogeneous ensemble learning and CAE outperformed methods implemented using ensemble learning without CAE. Based on the performance of filter-based methods, GR and Pearson correlation obtained most of the features in high rank scores (between 0.2 and 1) where these findings were merely on the influence of individual features [50]. In the analysis of learning behavior of students' college and its learning effect [44], the researchers used a threshold value based on the dependency value produced by Pearson correlation.…”

Section: Correlation-basedmentioning

confidence: 99%

“…In [50], two ensemble techniques, namely bagging and boosting were used to be integrated with classification models. In the experiment, only seven classification models were chosen whose performance was improved by employing 10-fold cross-validation.…”

Section: Embedded-basedmentioning

confidence: 99%

“…In [50], filter-based incorporates some search techniques, namely 'ranker' and 'greedy stepwise' for 'attribute evaluator'. A study regarding predicting the intention of using social media in online blended learning presented by [52], where data was obtained with 61 attributes, which were then minimized to 24 and 5 attributes, following the use of greedy technique and the wrapper method respectively.…”

Section: Search Techniquesmentioning

confidence: 99%

See 4 more Smart Citations

Feature selection techniques and classification algorithms for student performance classification: a review

Alias,

Hambali,

Abdul Aziz

et al. 2024

IJECE

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The process of categorizing students’ performance based on input data, encompassing demographic information and final exam results, is recognized as student performance classification. Educational data mining has gained traction in assessing students’ performance. However, this study entails the need to analyze the diverse attributes of students’ information within an educational institution by using data mining techniques. This study thoroughly examines both previous and current methodologies presented by researchers, addressing two main aspects: data preprocessing and classification algorithms applied in student performance classification. Data preprocessing specifically delves into the exploration of feature selection techniques, encompassing three types of feature selection and search methods. These techniques aim to identify the most significant features, eliminate unnecessary ones, and reduce data dimensionality. In addition, classification algorithms play a crucial role in categorizing or predicting student performance. Models such as k-nearest neighbors (KNN), decision tree (DT), artificial neural networks (ANN), and linear models (LR) were scrutinized based on their performance in prior research. Ultimately, this study highlights the potential for further exploration of feature selection techniques like information gain, Chi-square, and sequential selection, particularly when applied to new datasets such as students’ online learning activities, utilizing a variety of classification algorithms.

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