The effect of automated taxa identification errors on biological indices. (15th April 2017)

• Record Type: Journal Article
• Authors: Ärje, Johanna; Kärkkäinen, Salme; Meissner, Kristian; Iosifidis, Alexandros; Ince, Türker; Gabbouj, Moncef; Kiranyaz, Serkan
• Is Part Of: Expert systems with applications. Volume 72(2017)
• Page Start: 108
• Page End: 120
• Publication Date: 2017-04-15
• Subjects: Biomonitoring -- Classification error -- Diversity: error propagation -- Identification -- Similarity; Expert systems (Computer science) -- Periodicals; Systèmes experts (Informatique) -- Périodiques; Electronic journals; 006.33
• DOI: 10.1016/j.eswa.2016.12.015
• Languages: English
• ISSNs: 0957-4174

Abstract

Highlights: We study the effects of classification errors from 11 different classifiers on 14 biological indices. We assess the reasons why certain indices are more sensitive to classification errors. We compare the classifiers based on their ultimate use in the biomonitoring of macroinvertebrates. Abstract: In benthic macroinvertebrate biomonitoring systems, the target is to determine the status of ecosystems based on several biological indices. To increase cost-efficiency, computer-based taxa identification for image data has recently been developed. Taxa identification errors can, however, have strong effects on the indices and thus on the determination of the ecological status. In order to shift the biomonitoring process towards automated expert systems, we need a clear understanding on the bias caused by automation. In this paper, we examine eleven classification methods in the case of macroinvertebrate image data and show how their classification errors propagate into different biological indices. We evaluate 14 richness, diversity, dominance and similarity indices commonly used in biomonitoring. Besides the error rate of the classification method, we discuss the potential effect of different types of identification errors. Finally, we provide recommendations on indices that are least affected by the automatic identification errors and could be used in automated biomonitoring.