Distress classification of class-imbalanced inspection data via correlation-maximizing weighted extreme learning machine. (August 2018)

• Record Type: Journal Article
• Authors: Maeda, Keisuke; Takahashi, Sho; Ogawa, Takahiro; Haseyama, Miki
• Is Part Of: Advanced engineering informatics. Volume 37(2018)
• Page Start: 79
• Page End: 87
• Publication Date: 2018-08
• Subjects: Distress classification -- Civil structures -- Canonical correlation -- Extreme learning machine -- Class-imbalanced data -- Maintenance inspection; Computer-aided engineering -- Periodicals; Engineering -- Data processing -- Periodicals; 620.00285
• DOI: 10.1016/j.aei.2018.04.014
• Languages: English
• ISSNs: 1474-0346

Abstract

Abstract: This paper presents distress classification of class-imbalanced inspection data via correlation-maximizing weighted extreme learning machine (CMWELM). For distress classification, it is necessary to extract semantic features that can effectively distinguish multiple kinds of distress from a small amount of class-imbalanced data. In recent machine learning techniques such as general deep learning methods, since effective feature transformation from visual features to semantic features can be realized by using multiple hidden layers, a large amount of training data are required. However, since the amount of training data of civil structures becomes small, it becomes difficult to perform successful transformation by using these multiple hidden layers. On the other hand, CMWELM consists of two hidden layers. The first hidden layer performs feature transformation, which can directly extract the semantic features from visual features, and the second hidden layer performs classification with solving the class-imbalanced problem. Specifically, in the first hidden layer, the feature transformation is realized by using projections obtained by maximizing the canonical correlation between visual and text features as weight parameters of the hidden layer without designing multiple hidden layers. Furthermore, the second hidden layer enables successful training of our classifier by using weighting factors concerning the class-imbalanced problem. Consequently, CMWELM realizes accurate distress classification from a small amount of class-imbalanced data.