H.6. Pattern Recognition
J. Hamidzadeh
Abstract
In instance-based learning, a training set is given to a classifier for classifying new instances. In practice, not all information in the training set is useful for classifiers. Therefore, it is convenient to discard irrelevant instances from the training set. This process is known as instance reduction, ...
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In instance-based learning, a training set is given to a classifier for classifying new instances. In practice, not all information in the training set is useful for classifiers. Therefore, it is convenient to discard irrelevant instances from the training set. This process is known as instance reduction, which is an important task for classifiers since through this process the time for classification or training could be reduced. Instance-based learning methods are often confronted with the difficulty of choosing the instances which must be stored to be used during an actual test. Storing too many instances may result in large memory requirements and slow execution speed. In this paper, first, a Distance-based Decision Surface (DDS) is proposed which is used as a separating surface between the classes, then an instance reduction method, which is based on the DDS surface is proposed, namely IRDDS (Instance Reduction based on Distance-based Decision Surface). Using the DDS surface with Genetic algorithm selects a reference set for classification. IRDDS selects the most representative instances, satisfying both following objectives: high accuracy and reduction rates. The performance of IRDDS has been evaluated on real world data sets from UCI repository by the 10-fold cross-validation method. The results of the experiments are compared with some state-of-the-art methods, which show the superiority of the proposed method over the surveyed literature, in terms of both classification accuracy and reduction percentage.
H.3.15.3. Evolutionary computing and genetic algorithms
V. Majidnezhad
Abstract
In this paper, first, an initial feature vector for vocal fold pathology diagnosis is proposed. Then, for optimizing the initial feature vector, a genetic algorithm is proposed. Some experiments are carried out for evaluating and comparing the classification accuracies which are obtained by the use of ...
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In this paper, first, an initial feature vector for vocal fold pathology diagnosis is proposed. Then, for optimizing the initial feature vector, a genetic algorithm is proposed. Some experiments are carried out for evaluating and comparing the classification accuracies which are obtained by the use of the different classifiers (ensemble of decision tree, discriminant analysis and K-nearest neighbours) and the different feature vectors (the initial and the optimized ones). Finally, a hybrid of the ensemble of decision tree and the genetic algorithm is proposed for vocal fold pathology diagnosis based on Russian Language. The experimental results show a better performance (the higher classification accuracy and the lower response time) of the proposed method in comparison with the others. While the usage of pure decision tree leads to the classification accuracy of 85.4% for vocal fold pathology diagnosis based on Russian language, the proposed method leads to the 8.5% improvement (the accuracy of 93.9%).
