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Nowadays, the classification problems have become more challenging due to the various types of data set. Some data are appropriated for machine learning techniques and some data are appropriated for statistical leaning techniques. This work proposes a new hybrid ensemble of machine and statistical learning models using confidence-based boosting. The proposed method which uses variants of based classifiers...
In this paper, a new method for question classification is proposed, which employs ensemble learning algorithms to train multiple question classifiers. These component learners are combined to produce the final hypothesis. In detail, the feature spaces are obtained through extracting high-frequency keywords from questions corpus and the method of word semantic similarity is performed to adjust the...
In this study, we applied ensemble machine learning to evaluate credit scoring. With decision tree as the baseline algorithm, two popular ensemble learning methods, bagging and boosting, were evaluated across different experiment conditions: using all 14 features, using selected 6 features on Australian credit data form UCI data set. Results showed that in experiments with all features improved performance...
Classification, which is the task of assigning object (event) to one of several predefined classes, is an important problem in the field of machine learning and data mining. Boosting based technique use deterministic results of weak classifiers to compound them to a strong classifier while classifier can give class distribution as result. It involves losses of information. This problem can be solved...
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