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

Abstract

<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>

Data Mining K-Means Algorithm for Performance Analysis

“…Neural networks have a lot of layers where there are also hidden layers that generally have sigmoid activation functions. Then, the next technique is decision tree modeling technique used to predict and analyze data, where all attributes are known and defined [15]. Rules that define a transaction that contains the occurrence of an item through the form X → Y can be referred to as an association rule.…”

Classification of Secondary School Destination for Inclusive Students using Decision Tree Algorithm

“…At this point, the researcher used a confusion matrix to evaluate the decision support system's performance in classifying the secondary school for inclusive students. Accuracy, precision, and recall can be used to evaluate the performance of decision support systems [30].…”

Comparison of the feature selection algorithm in educational data mining

“…This paper using six feature selection algorithms have been tested before, there are Cfs subset eval [15], Chi squared attribute eval [16], filtered attribute eval [17], gain ratio attribute eval [18], principal components [19], and relief attribute eval [20]. This paper also uses 15 different classification algorithms that have been tested through educational datasets, specifically Bayes net, Naïve Bayes, Naive Bayes updateable, multilayer perceptron, simple logistic, SMO, decision tree, JRip, OneR, PART, decision stump, J48, random forest, random tree, and REP tree [21]- [23].…”