scholarly journals Using Predictive Modeling System and Ensemble Method to Ameliorate Classification Accuracy in EDM

2018 ◽  
Vol 7 (2) ◽  
pp. 44-47
Author(s):  
Mudasir Ashraf ◽  
Majid Zaman ◽  
Muheet Ahmed
Keyword(s):  
Data Mining ◽  
Educational Data Mining ◽  
Ensemble Methods ◽  
Ensemble Classifiers ◽  
Learning Classifiers ◽  
Meta Learning ◽  
Significant Phenomenon ◽  
Increasing Demand ◽  
Analogous Method ◽  
Fold Cross Validation

Educational data mining has illustrated an increasing demand for extracting and maneuvering data from academic backdrop, to generate prolific information which is indispensible for decision making. Therefore in this paper, an attempt has been made to deploy various data mining techniques including base and meta learning classifiers across our pedagogical dataset to foretell the performance of students. Among several contemporary ensemble approaches, researchers have practiced widespread learning classifiers viz. boosting to predict the performance of students. As exploitation of ensemble methods is considered to be significant phenomenon in classification and prediction mechanisms, therefore analogous method (boosting) has been applied across our pedagogical dataset. The entire results have been evaluated with 10-fold cross validation, once pedagogical dataset has been subjected to base classifiers including j48, random tree, naive bayes and knn. In addition, techniques such as oversampling (SMOTE) and undersampling (Spread subsampling) have been employed to further draw a comparison among ensemble classifiers and base classifiers. These methods were exploited with the key objective to observe any improvement in prediction accuracy of students.

scholarly journals Developed third iterative dichotomizer based on feature decisive values for educational data mining

2020 ◽  
Vol 18 (1) ◽  
pp. 209
Author(s):  
Saja Taha Ahmed ◽  
Rafah Al-Hamdani ◽  
Muayad Sadik Croock
Keyword(s):  
Data Mining ◽  
Feature Selection ◽  
Decision Tree ◽  
Predictive Analytics ◽  
Educational Data Mining ◽  
Target Class ◽  
Id3 Algorithm ◽  
Feature Weight ◽  
Holdout Validation ◽  
Fold Cross Validation

<p><span>Recently, the decision trees have been adopted among the preeminent utilized classification models. They acquire their fame from their efficiency in predictive analytics, easy to interpret and implicitly perform feature selection. This latter perspective is one of essential significance in Educational Data Mining (EDM), in which selecting the most relevant features has a major impact on classification accuracy enhancement. <br /> The main contribution is to build a new multi-objective decision tree, which can be used for feature selection and classification. The proposed Decisive Decision Tree (DDT) is introduced and constructed based on a decisive feature value as a feature weight related to the target class label. The traditional Iterative Dichotomizer 3 (ID3) algorithm and the proposed DDT are compared using three datasets in terms of some ID3 issues, including logarithmic calculation complexity and multi-values features<em></em>selection. The results indicated that the proposed DDT outperforms the ID3 in the developing time. The accuracy of the classification is improved on the basis of 10-fold cross-validation for all datasets with the highest accuracy achieved by the proposed method is 92% for the student.por dataset and holdout validation for two datasets, i.e. Iraqi and Student-Math. The experiment also shows that the proposed DDT tends to select attributes that are important rather than multi-value. </span></p>

scholarly journals Analyzing performance of classifiers for medical datasets

2018 ◽  
Vol 7 (2.15) ◽  
pp. 136 ◽  
Author(s):  
Rosaida Rosly ◽  
Mokhairi Makhtar ◽  
Mohd Khalid Awang ◽  
Mohd Isa Awang ◽  
Mohd Nordin Abdul Rahman
Keyword(s):  
Breast Cancer ◽  
Cross Validation ◽  
Ensemble Methods ◽  
Data Sets ◽  
Ensemble Classifiers ◽  
Classification Models ◽  
Data Set ◽  
Mining Tool ◽  
Fold Cross Validation

This paper analyses the performance of classification models using single classification and combination of ensemble method, which are Breast Cancer Wisconsin and Hepatitis data sets as training datasets. This paper presents a comparison of different classifiers based on a 10-fold cross validation using a data mining tool. In this experiment, various classifiers are implemented including three popular ensemble methods which are boosting, bagging and stacking for the combination. The result shows that for the classification of the Breast Cancer Wisconsin data set, the single classification of Naïve Bayes (NB) and a combination of bagging+NB algorithm displayed the highest accuracy at the same percentage (97.51%) compared to other combinations of ensemble classifiers. For the classification of the Hepatitisdata set, the result showed that the combination of stacking+Multi-Layer Perception (MLP) algorithm achieved a higher accuracy at 86.25%. By using the ensemble classifiers, the result may be improved. In future, a multi-classifier approach will be proposed by introducing a fusion at the classification level between these classifiers to obtain classification with higher accuracies.  

An Insight into Educational Data Mining

2019 ◽  
Vol 7 (2) ◽  
pp. 83-90
Author(s):  
Balwinder Kaur ◽  
Anu Gupta ◽  
R.K.Singla .
Keyword(s):  
Data Mining ◽  
Educational Data Mining ◽  
Insight Into

Rancang Bangun Sistem Informasi Untuk Menentukan Kapabilitas Konsumen Dalam Mengambil Pinjaman KPR

2016 ◽  
Vol 7 (2) ◽  
pp. 75-80
Author(s):  
Adhi Kusnadi ◽  
Risyad Ananda Putra
Keyword(s):  
Data Mining ◽  
Low Income ◽  
Cross Validation ◽  
Classification Tree ◽  
Large Population ◽  
Housing Development ◽  
Good Precision ◽  
Index Terms ◽  
The Government ◽  
Fold Cross Validation

Indonesia is one country that has a relatively large population . The government in the period of 5 years, annually hold a procurement program 1 million FLPP house units. This program is held in an effort to provide a decent home for low income people. FLPP housing development requires good precision and speed of development on the part of the developer, this is often hampered by the bank process, because it is difficult to predict the results and speed of data processing in the bank. Knowing the ability of consumers to get subsidized credit, has many advantages, among others, developers can plan a better cash flow, and developers can replace consumers who will be rejected before entering the bank process. For that reason built a system that can help developers. There are many methods that can be used to create this application. One of them is data mining with Classification tree. The results of 10-fold-cross-validation applications have an accuracy of 92%. Index Terms-Data Mining, Classification Tree, Housing, FLPP, 10-fold-cross Validation, Consumer Capability

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