ABSTRACT
An order task starts with a data set where the class tasks are known. A dataset with fewer elements, which causes the aggregation run of a classifier to decrease. This paper suggests two quality development strategies for a data set. The class plausibility construction technique is used for highlights with a weak crossing region and the manufacturing component development strategy is used for elements with a high crossing region. An attempt is made to analyses the presentation of the proposed technique using four data sets with two classes and four data sets with several classes with different stroke sizes. The results show that the proposed technique has a higher order execution with Support Vector Machine (SVM) classifier when compared with K-nearest neighbor (KNN) classifier.
Index Terms
- Improving Classifier Efficiency by Expanding Number of Functions in the Dataset
Recommendations
Building Locally Discriminative Classifier Ensemble Through Classifier Fusion Among Nearest Neighbors
PCM 2016: 17th Pacific-Rim Conference on Advances in Multimedia Information Processing - Volume 9916Many studies on ensemble learning that combines multiple classifiers have shown that, it is an effective technique to improve accuracy and stability of a single classifier. In this paper, we propose a novel discriminative classifier fusion method, which ...
Chinese text classification by the Naïve Bayes Classifier and the associative classifier with multiple confidence threshold values
Each type of classifier has its own advantages as well as certain shortcomings. In this paper, we take the advantages of the associative classifier and the Naive Bayes Classifier to make up the shortcomings of each other, thus improving the accuracy of ...
Comments