Large Scale Hierarchical Classification
Large Scale Hierarchical Classification
复制标题
大规模层次分类
DOI：
10.31645/2013.11.2.3
复制
发表时间：
2013
期刊：
影响因子：
--
通讯作者：
Rahim Hasnani
中科院分区：
文献类型：
--
作者：
Adarsh Khalique;Rahim Hasnani
这项研究阐明了用于文档或文本分类挑战的各种算法。本研究使用样本数据，使用支持向量机 (SVM)、朴素贝叶斯、神经网络和 K 最近邻等各种算法来分析其性能和准确性。本研究试图确定这些算法在给定样本数据上的局限性和强度，以及它们如何以最佳方式执行分类。本研究使用不同的验证来检查可以识别的分类的准确性。验证包括分割验证、X 验证和引导。讨论了在大层次结构中进行分类的不同方式和方法。最后，本研究根据所获得的结果得出结论，哪种机器学习技术或分类器在提供的样本数据集上表现出色，并且与其他技术或分类器相比实现了更高的准确性。
This study elucidates various algorithms used for document or text classification challenge. A sample data is used in this study on which various algorithms like Support Vector Machines (SVM), Naïve Bayes, Neural Networks and K-Nearest Neighbor are used in order to analyze their performances and accuracies. This study tries to identify the limitations and strength of these algorithms on the given sample data that how optimally they can perform classification. Different validations are used in this study to examine the accuracies regarding the classification can be identified. Validations include Split-Validation, X-Validation and Bootstrapping. Different ways and methods are discussed through which classification is made possible in large hierarchy. Finally this study concludes on the basis of results obtained that which machine learning technique or classifier performed excellent on the provided sample data set and achieved higher accuracy as compared to others.