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ae Newly developed web-based educational technologies offer researchers unique opportunities to study how students learn and what approaches to learning lead to success. Web-based systems routinely collect vast quantities of data on user patterns, and data mining methods can be applied to these databases. This paper presents an approach to classifying(More)
Many stability measures, such as Normalized Mutual Information (NMI), have been proposed to validate a set of partitionings. It is highly possible that a set of partitionings may contain one (or more) high quality cluster(s) but is still adjudged a bad cluster by a stability measure, and as a result, is completely neglected. Inspired by evaluation(More)
This paper presents an approach for classifying students in order to predict their final grade based on features extracted from logged data in an education web-based system. A combination of multiple classifiers leads to a significant improvement in classification performance. Through weighting the feature vectors using a Genetic Algorithm we can optimize(More)
Web-based educational technologies allow educators to study how students learn (descriptive studies) and which learning strategies are most effective (causal/predictive studies). Since web-based educational systems collect vast amounts of student profile data, data mining and knowledge discovery techniques can be applied to find interesting relationships(More)
Clustering ensembles combine multiple partitions of data into a single clustering solution of better quality. Inspired by the success of supervised bagging and boosting algorithms, we propose non-adaptive and adaptive resampling schemes for the integration of multiple independent and dependent clusterings. We investigate the effectiveness of bagging(More)
Phishing attack is a kind of identity theft which tries to steal ?confidential data like on?-?line bank account information?. In a ?phishing attack scenario, attacker deceives users by a fake email ?which is called scam. In this paper we employ three different ?learning methods to detect phishing scams. Then, we use ?ensemble methods on their results to(More)
In this paper, a new combinational method for improving the recognition rate of multiclass classifiers is proposed. The main idea behind this method is using pairwise classifiers to enhance the ensemble. Because of more accuracy of them, they can decrease the error rate in error-prone feature space. Firstly, a multiclass classifier has been trained. Then,(More)
In this paper a new criterion for clusters validation is proposed. Many stability measures to validate a cluster have been proposed such as Normalized Mutual Information. We propose a new criterion for clusters validation. The drawback of the common approach is discussed in this paper and then a new asymmetric criterion is proposed to assess the association(More)
In this paper a new criterion for clusters validation is proposed. This new cluster validation criterion is used to approximate the goodness of a cluster. The clusters which satisfy a threshold of this measure are selected to participate in clustering ensemble. For combining the chosen clusters, a co-association based consensus function is applied. Since(More)
In the past decade many new methods were proposed for combining multiple classifiers. Neural network ensemble is a learning paradigm where many neural networks are jointly used to solve a problem. We propose a GA-based method for constructing a neural network ensemble using a weighted vote-based classifier selection approach. Main presumption of this method(More)