Multi-view ensemble learning: an optimal feature set partitioning for high-dimensional data classification. Issue 1 (October 2016)

• Record Type: Journal Article
• Authors: Kumar, Vipin; Minz, Sonajharia
• Is Part Of: Knowledge and information systems. Volume 49:Issue 1(2016:Oct.)
• Page Start: 1
• Page End: 59
• Publication Date: 2016-10
• Subjects: Classification -- Feature set partitioning -- High dimensionality -- Multi-view ensemble learning; Expert systems (Computer science) -- Periodicals; Information storage and retrieval systems -- Periodicals; 006.33
• DOI: 10.1007/s10115-015-0875-y
• Languages: English
• ISSNs: 0219-1377

Abstract

Abstract Multi-view ensemble learning has the potential to address issues related to the high dimensionality of data. It attempts to utilize all the relevant only discarding the irrelevant features. The view of a dataset is the sub-table of the training data with respect to a subset of the feature set. The problem of discarding the irrelevant features and obtaining subsets of the relevant features is useful for dimension reduction and dealing with the problem of having fewer training examples than even the reduced set of relevant features. A feature set partitioning resulting in the blocks of relevant features may not yield multiple-view-based classifiers with good classification performance. In this work the optimal feature set partition approach has been proposed. Further, the ensemble learning from views aims to maximize the performance of the classifier. The experiments study the performance of random feature set partitioning, attribute bagging, view generation using attribute clustering, view construction using genetic algorithm and OFSP proposed method. The blocks of relevant feature subsets are used to construct the multi-view classifier ensemble using K-nearest neighbor, Naïve Bayesian and support vector machine algorithm applied to sixteen high-dimensional data sets from UCI machine learning repository. The performance parameters considered for comparison are classification accuracy, disagreement among the classifiers, execution time and percentage reduction of attributes.