Using Ensemble StackingC Method and Base Classifiers to Ameliorate Prediction Accuracy of Pedagogical Data

Ashraf, Mudasir; Zaman, Majid; Ahmed, Muheet

doi:10.1016/j.procs.2018.05.018

Cited by 26 publications

(12 citation statements)

References 17 publications

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“…This described that the proposed method was feasible to improve the accuracy especially the precision and the sensitivity of each class in the tracer study dataset for classifications of the relevance of education background with graduate employment. In Table 7, the accuracy of data in [17], [18], [27]- [28] was more than 90% but the increase of accuracy was less than other studies. Combination of SMOTE with hybrid scheme [18] increase accuracy than original SMOTE that applied in [11], [17], [27] and SMOTE that combine with axiomatic fuzzy SMOTE [28].…”

mentioning

confidence: 82%

“…Thammasiri et al [27] applied original SMOTE with KNN as the classification method, the accuracy of the study was increased but for the precision and the sensitivity of the study actually dropped considerably compared to the results of the precision and sensitivity before SMOTE. The improving SMOTE with attribute weighted SMOTE [29] has given better the increase of precision and sensitivity than the increase precision and sensitivity in other studies that applied original SMOTE [17], [30] and combined SMOTE with gaussian distribution [20]. The proposed method that handles the imbalanced data with the missing value elimination SMOTE had the highest increases of accuracy, precision and sensitivity even though the accuracy was not the highest compared with other studies.…”

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confidence: 99%

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Handling the imbalanced data with missing value elimination SMOTE in the classification of the relevance education background with graduates employment

Desiani

Yahdin

Kartikasari

et al. 2021

IJ-AI

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<span id="docs-internal-guid-cd6caed5-7fff-5f99-0341-cb32fa5ad787"><span>The imbalanced data affect the accuracy of models, especially for precision and sensitivity, it makes difficult to find information on minority class. The problem is identified in the tracer study dataset Universitas Sriwijaya that has 2934 data. The label attribute is divided into several label classes, namely not tight, somewhat-tight, tight, very tight, and tightest. The number of the tightest and very tight is 27% and 38.6% of the number majority classes. In the study, the SMOTE is combined with eliminating the missing value of data to handle the imbalanced data. The method was evaluated by the classification methods KNN, ANN, and C4.5. The results of these methods show a significant increase in accuracy as a whole and a significant increase in the precision and sensitivity of minority classes. The precision and sensitivity of both the majority and minority are not too different, although the number of the minority is very less compared to the majority class. the information on minority classes can be obtained with quite high precision and sensitivity. As a conclusion, the proposed method is passably to improve accuracy and greatly affects the increase in sensitivity and precision.</span></span>

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confidence: 82%

mentioning

confidence: 99%

Handling the imbalanced data with missing value elimination SMOTE in the classification of the relevance education background with graduates employment

Desiani

Yahdin

Kartikasari

et al. 2021

IJ-AI

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show abstract

“…Table 4 shows the variables found that belong to the factor and the authors who used some of these for the development of their models. [22], [23], [26], [28]- [30], [35], [38]- [40], [42]- [44], [46], [49]- [52], [55], [57], [58], [60], [61], [63]- [68],…”

Section: Academic Management Factormentioning

confidence: 99%

revisión sobre la predicción del rendimiento académico mediante métodos de ensamble

Rodríguez¹,

Silva²,

Bravo³

2022

ing. Solidar

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Introducción: El presente artículo es producto de la investigación “Métodos de ensamble para estimar el ren-dimiento académico de estudiantes de educación superior”, desarrollado en la Universidad Distrital Francisco José de Caldas en el año 2021 y se centra en la revisión de trabajos de investigación desarrollados en los últimos cinco años relacionados a la predicción del rendimiento académico utilizando algoritmos de ensamble.Objetivo: La revisión de la literatura tiene como objetivo identificar los algoritmos más utilizados y las variables más relevantes en la predicción del rendimiento académico.Metodología: Se realizó una revisión sistemática de la literatura en distintas bases de datos académicas (Science Direct, Scopus, SAGE Journals, EBSCO, ResearchGate, Google Scholar), utilizando ecuaciones de bús-queda construidas con palabras claves.Resultados: Se encontraron 54 artículos relacionados que cumplen con los criterios de inclusión de la revisión. Además, se encontraron beneficios en la aplicación de métodos de ensamble en la predicción del rendimiento académico. Conclusión: Se encontró que las variables más influyentes en el rendimiento académico corresponden al factor académico, el algoritmo utilizado que presenta mejores resultados es Random Forest, además de que fue el más utilizado, y que el uso de estos algoritmos es una herramienta precisa para predecir el rendimiento acadé-mico en cualquier etapa de la vida universitaria, y a su vez brindar la información para generar estrategias que permitan mejorar los indicadores de deserción y retención académica.

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“…Since, a lot of data is present in every field like academic data [29][30][31][32][33], agricultural data [34], weather data [35][36][37][38], cloud data [39] and other type of data [40][41][42][43][44][45][46][47] etc. Here, in this study the dataset used in this operation was collected from 2012-2017 of Kashmir province.…”

Section: 21the Modelmentioning

confidence: 99%

An application of logistic model tree (LMT) algorithm to ameliorate Prediction accuracy of meteorological data

Fayaz¹,

Zaman²,

Butt³

2021

IJATEE

Self Cite

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Weather forecasting is considered as one of the approaches which are used to check the state of the atmosphere in the future at a specified location. A type of weather forecasting called as rainfall prediction has the highest influence in the farming & agricultural sector and other various sectors like natural disaster management etc. Accurate and timely rainfall prediction is one of the important factors in today's environment. Since the rainfall parameters keep on changing around the world in different places have different factors which are responsible for the rainfall. These parameters include the wind, vapour pressure, humidity at different intervals, temperature, season, evaporation etc. and are considered as the important meteorological parameters for predicting the rainfall in future time [1].*Author for correspondence Decision tree algorithms have been a choice of tool in the machine learning algorithm over the past decade. It was followed up by its modified version, including the more successful one i.e., random forest. However, there has been more precision and accuracy in modified versions of the various decision tree Re search Article

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Using Ensemble StackingC Method and Base Classifiers to Ameliorate Prediction Accuracy of Pedagogical Data

Cited by 26 publications

References 17 publications

Handling the imbalanced data with missing value elimination SMOTE in the classification of the relevance education background with graduates employment

Handling the imbalanced data with missing value elimination SMOTE in the classification of the relevance education background with graduates employment

revisión sobre la predicción del rendimiento académico mediante métodos de ensamble

An application of logistic model tree (LMT) algorithm to ameliorate Prediction accuracy of meteorological data

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