A Survey of Decision Fusion and Feature Fusion Strategies for Pattern Classification
A Survey of Decision Fusion and Feature Fusion Strategies for Pattern Classification
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DOI：
10.4103/0256-4602.64604
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发表时间：
2010-07-01
影响因子：
2.4
通讯作者：
Chowdhury, Pinaki Roy
中科院分区：
文献类型：
--
作者：
Mangai, Utthara Gosa;Samanta, Suranjana;Chowdhury, Pinaki Roy
对于任何模式分类任务，数据大小、类数量、特征空间维度和类间可分离性的增加都会影响任何分类器的性能。单个分类器通常无法处理任何问题域中数据的广泛变化和可扩展性。大多数现代模式分类技术使用分类器的组合并融合分类器提供的决策，通常仅使用一组选定的适合任务的特征。在特征选择和融合任务中解决了选择一组有用的特征并丢弃不提供类可分离性的特征的问题。本文回顾了模式分类任务中决策融合和特征融合策略中使用的不同技术和算法。对用于决策融合、特征选择和融合技术的重要技术的调查已单独讨论。根据分类所采用的适用性和方法对用于融合的不同技术进行了分类。我们提出了一种新颖的框架，结合了决策融合和特征融合的概念来提高分类的性能。在三个基准数据集上进行了实验，以证明特征融合和决策融合技术相结合的鲁棒性。
For any pattern classification task, an increase in data size, number of classes, dimension of the feature space, and interclass separability affect the performance of any classifier. A single classifier is generally unable to handle the wide variability and scalability of the data in any problem domain. Most modern techniques of pattern classification use a combination of classifiers and fuse the decisions provided by the same, often using only a selected set of appropriate features for the task. The problem of selection of a useful set of features and discarding the ones which do not provide class separability are addressed in feature selection and fusion tasks. This paper presents a review of the different techniques and algorithms used in decision fusion and feature fusion strategies, for the task of pattern classification. A survey of the prominent techniques used for decision fusion, feature selection, and fusion techniques has been discussed separately. The different techniques used for fusion have been categorized based on the applicability and methodology adopted for classification. A novel framework has been proposed by us, combining both the concepts of decision fusion and feature fusion to increase the performance of classification. Experiments have been done on three benchmark datasets to prove the robustness of combining feature fusion and decision fusion techniques.