Large-Scale Hierarchical Classification with Feature Selection
Large-Scale Hierarchical Classification with Feature Selection
复制标题
具有特征选择的大规模层次分类
DOI：
10.1007/978-3-030-01620-3_4
复制
发表时间：
2018
影响因子：
2.4
通讯作者：
H. Rangwala
中科院分区：
文献类型：
--
作者：
Azad Naik;H. Rangwala
LSHC涉及由数千个类和数百万个具有高维特征的训练实例组成的数据集，提出了几个大数据挑战。特征选择旨在选择判别特征的子集，是处理大规模问题的一种有效策略。它加快了训练过程，减少了预测时间，并通过压缩学习到的模型权重向量的总大小来最小化内存需求。大多数研究也表明，特征选择是胜任的，并成功地通过去除不相关的特征来提高分类精度。在本章中，我们研究了各种基于滤波器的特征选择降维方法来解决LSHC问题。
LSHC involves dataset consisting of thousands of classes and millions of training instances with high-dimensional features posing several big data challenges. Feature selection that aims to select the subset of discriminant features is an effective strategy to deal with large-scale problem. It speeds up the training process, reduces the prediction time, and minimizes the memory requirements by compressing the total size of learned model weight vectors. Majority of the studies have also shown feature selection to be competent and successful in improving the classification accuracy by removing irrelevant features. In this chapter, we investigate various filter-based feature selection methods for dimensionality reduction to solve the LSHC problem.