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Researchers in higher education are beginning to explore the potential of data mining in analyzing data for the purpose of giving quality service and needs of their graduates. Thus, educational data mining emerges as one tools to study academic data to identify patterns and help for decision making affecting the education. This paper predicts the employability of IT graduates using nine variables...
This paper proposes a short-term energy price classification model using decision tree. The proposed model does not predict the exact value of future electricity price, but the class to which it belongs, established with respect to pre-specified threshold. This strategy is proposed since for some applications, the exact value of future prices is not required for the decision-making process. A feature...
Changes in the network topology such as large-scale power outages or Internet worm attacks are events that may induce routing information updates. Border Gateway Protocol (BGP) is by Autonomous Systems (ASes) to address these changes. Network reachability information, contained in BGP update messages, is stored in the Routing Information Base (RIB). Recent BGP anomaly detection systems employ machine...
Data mining approaches have been used in business purposes since its inception; however, at present it is used successfully in new and emerging areas like education systems. Government of Bangladesh emphasizes the need to improve the education system. In this research, we use data mining approaches to predict students' final outcome, i.e., final grade in a particular course by overcoming the problem...
Post-pruning is a common method of decision tree pruning. However, various post-pruning tends to use a single measure as an evaluation standard of pruning effects. The single and exclusive index evaluation standard of decision tree is subjective and partial, and the decisions after pruning often have a bias. This paper proposes a decision tree post-pruning algorithm based on comprehensive considering...
The paper aims to develop the predictive models for dengue outbreak detection using Multiple Rule Based Classifiers. The rule based classifiers used are the Decision Tree, Rough Set Classifier, Naive Bayes, and Associative Classifier. Dengue fever (DF) and dengue hemorrhagic fever (DHF) have been continuously becoming a public health related issues in Malaysia and growing pandemic as reported by World...
The credit scoring has been regarded as a critical topic and its related departments make efforts to collect huge amount of data to avoid wrong decision. An effective classificatory model will objectively help managers instead of intuitive experience. This study proposes five approaches combining with the back-propagation neural network (BPN) classifier for features selection that retains sufficient...
During several table tennis matches, the prediction of outcomes is of a major interest to coaches to arrange suitable and effective trainings. The purpose of this investigation is to propose a new approach of combination to predict the outcome of matches. The artificial neural network (ANN)is capable of efficient data fitting, as the decision tree is capable of data reduction and classification. We...
Recently, DoS (Denial of Service) detection has become more and more important in web security. In this paper, we argue that DoS attack can be taken as continuous data streams, and thus can be detected by using stream data mining methods. More specifically, we propose a new Weighted Ensemble learning model to detect the DoS attacks. The Weighted Ensemble model first trains base classifiers using different...
For classification problems in data mining based on the thought of combination classification method, this paper proposes a combination classification method of multiple decision trees, which was based on genetic algorithm. In the proposed combination classification method, multiple decision trees that adopt the method of probability measurement level output are parallel combined. Then genetic algorithm...
Different biological labs conduct similar experiments on same diseases. It is highly desirable to have a better model based on more experimental results than that on a single result. In this paper, we propose a method for integrating microarray data from multiple sources for building classification models. To test the method, we use three real world microarray data sets from different labs with different...
Data preprocessing is an important data manipulation process prior to mining actions. Various techniques that include feature selection and data transformation have been studied in the past, with the aim of producing a compact and efficient decision tree. They all have their respective strengths, but in general they commonly lack of preserving the meanings of the attributes. The concept of Attribute...
Classification is one of the tasks in data mining. Nowadays, there are many classification techniques being used to solve classification problems such as neural network, genetic algorithm, Bayesian and others. In this article, we attempt to present a study on how talent management can be implemented using decision tree induction techniques. By using this approach, talent performance can be predicted...
The development of credit scoring model has been regarded as a critical topic. This study proposed four approaches combining with the KNN (K-nearest neighbor) classifier for features selection that retains sufficient information for classification purpose. Two UCI data sets and different models combined with KNN classifier were constructed by selecting features. KNN classifier combines with conventional...
In this paper we explore the use of weights in the generation of fuzzy models. We automatically generate a fuzzy model, using a three-stage methodology: (i) generation of a crisp model from a decision tree, induced from the data, (ii) transformation of the crisp model into a fuzzy one, and (iii) optimization of the fuzzy modelpsilas parameters. Based on this methodology, the generated fuzzy model...
In this paper we present a comparative analysis of the predictive power of two different sets of metrics for defect prediction. We choose one set of product related and one set of process related software metrics and use them for classifying Java files of the Eclipse project as defective respective defect-free. Classification models are built using three common machine learners: logistic regression,...
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