Abstract. Data mining is the process of extracting useful information from a huge amount of data. One of the most common applications of data mining is the use of different algorithms and tools to estimate future events based on previous experiences. In this context, many researchers have been using data mining techniques to support and solve challenges in higher education. There are many challenges facing this level of education, one of which is helping students to choose the right course to improve their success rate. An early prediction of students' grades may help to solve this problem and improve students' performance, selection of courses, success rate and retention. In this paper we use different classification techniques in order to build a performance prediction model, which is based on previous students' academic records. The model can be easily integrated into a recommender system that can help students in their course selection, based on their and other graduated students' grades. Our model uses two of the most recognised decision tree classification algorithms: ID3 and J48. The advantages of such a system have been presented along with a comparison in performance between the two algorithms.
Many studies uses different data mining techniques to analyze mass spectrometry data and extract useful knowledge about biomarkers. These Biomarkers allow the medical experts to determine whether an individual has a disease or not. Some of these studies have proposed models that have obtained high accuracy. However, the black-box nature and complexity of the proposed models have posed significant issues. Thus, to address this problem and build an accurate model, we use a genetic algorithm for feature selection along with a rule-based classifier, namely Genetic Rule-Based Classifier algorithm for Mass Spectra data (GRC-MS). According to the literature, rule-based classifiers provide understandable rules, but not accurate. In addition, genetic algorithms have achieved excellent results when used with different classifiers for feature selection. Experiments are conducted on real dataset and the proposed classifier GRC-MS achieves 99.7% accuracy. In addition, the generated rules are more understandable than those of other classifier models.
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