In many pattern recognition applications feature selection and instance selection can be used as two data preprocessing methods that aim at reducing the computational cost of the learning process. Moreover, in some cases, feature subset selection can improve the classification performance. Feature selection and instance selection can be interesting since the choice of features and instances greatly influence the performance of the learnt models as well as their training costs. In the past, unifying both problems was carried out by solving a global optimization problem using meta-heuristics. This paradigm not only does not exploit the manifold structure of data but can be computationally expensive. To the best of our knowledge, the joint use of sparse modeling representative and feature subset relevance have not been exploited by the joint feature and selection methods. In this paper, we target the joint feature and instance selection by adopting feature subset relevance and sparse modeling representative selection. More precisely, we propose three schemes for the joint feature and instance selection. The first is a wrapper technique while the two remaining ones are filter approaches. In the filter approaches, the search process adopts a genetic algorithm in which the evaluation is mainly given by a score that quantify the goodness of the features and instances. An efficient instance selection technique is used and integrated in the search process in order to adapt the instances to the candidate feature subset. We evaluate the performance of the proposed schemes using image classification where classifiers are the nearest neighbor classifier and support vector machine classifier. The study is conducted on five public image datasets. These experiments show the superiority of the proposed schemes over various baselines. The results confirm that the filter approaches leads to promising improvement on classification accuracy when both feature selection and instance selection are adopted.

中文翻译：

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使用流形数据标准的联合特征和实例选择：在图像分类中的应用

在许多模式识别应用中，特征选择和实例选择可以用作两种数据预处理方法，旨在降低学习过程的计算成本。此外，在某些情况下，特征子集选择可以提高分类性能。特征选择和实例选择可能很有趣，因为特征和实例的选择极大地影响了学习模型的性能及其训练成本。过去，通过使用元启发式解决全局优化问题来统一这两个问题。这种范式不仅没有利用数据的流形结构，而且在计算上可能很昂贵。据我们所知，联合特征和选择方法尚未利用稀疏建模代表和特征子集相关性的联合使用。在本文中，我们通过采用特征子集相关性和稀疏建模代表选择来针对联合特征和实例选择。更准确地说，我们为联合特征和实例选择提出了三种方案。第一个是包装技术，而剩下的两个是过滤器方法。在过滤器方法中，搜索过程采用遗传算法，其中评估主要由量化特征和实例的好坏的分数给出。在搜索过程中使用并集成了一种有效的实例选择技术，以便使实例适应候选特征子集。我们使用图像分类来评估所提出方案的性能，其中分类器是最近邻分类器和支持向量机分类器。该研究是在五个公共图像数据集上进行的。这些实验表明所提出的方案在各种基线上的优越性。结果证实，当同时采用特征选择和实例选择时，过滤器方法可以显着提高分类精度。