classification is the process of automatically creating a model of classes from a set of records that contain class labels. decision tree classification. intelligent miner
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in this paper, we use two data mining classifiers, nave based and j48, in order to classify a set of test cases and identify which test case is redundant or
privacy preserving data mining classifier for smart city applications. abstract: the life style of people are changing day by day due to the paradigm shift in
moreover, our algorithm requires only limited feedback of the mining performance to enable the learning of the optimal classifier chain. we model the learning
educational data mining: classifier comparison for the course selection process. abstract: the education system in india & across the world has shown a
performance analysis of data mining classification techniques to predict diabetes data mining techniques for diabetes prediction and course of progression.
the paper uses data mining approaches to classify bug types and excavate debug strategy association rules for web-based applications. chi-square algorithm
combining multiple data mining classifier improve the performance of technique? 2.if so ,how to hybrid different classifier together ? 3.which
ensemble classifier data mining. difficulty level : easy; last updated : 30 may, 2019. ensemble learning helps improve machine learning results by
the derived tweets will process in text mining and then analyzed by classification process using naive bayes classifier, nearest neighbor, and decision tree to
with the classification algorithms, you can create, validate, or test classification models. for example, you can analyze why a certain classification was made,
classification is the process of predicting the class of given data points. classes are sometimes called as targets/ labels or categories. classification predictive
weighted associative classifiers that takes advantage of weighted association rule mining is already being proposed . the experiments on bench mark
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it has been found that whenever correct attributes are selected before classification, accuracy of data mining algorithms is improved significantly [
the classifiers in the ensemble are judiciously weighted based on their expected classification accuracy on the test data under the time-evolving
learning classifier systems, or lcs, are a paradigm of rule-based machine learning methods it can be an offline, finite training dataset (characteristic of a data mining, classification, or regression problem), or an online sequential stream of
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one of the most important techniques of data mining is classification which is used for prediction purposes. in this paper, we present a novel classifier for
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