Abstract:
This paper describes a new concept of multi-stage classification with intermediate learning (MSIL), and validates a simple two-stage version of the MSIL on nine popular t...Show MoreMetadata
Abstract:
This paper describes a new concept of multi-stage classification with intermediate learning (MSIL), and validates a simple two-stage version of the MSIL on nine popular test datasets. The first stage performs classical learning and inference based on features calculated directly from the data. The second stage learns and infers the final diagnosis using diagnostic labels generated at the first stage. Since both stages are trained independently, the learning results of the second stage do not alter the learning results accomplished at the first stage. This important property enables the generation of more complex, multi-channel and/or multi-level machine reasoning systems consisting of algebraically connected basic two-stage units. Classification tests showed that in almost all tested cases, the accuracy achieved at the first stage was further improved by the second stage of classification. This means that primary learning from the data can be improved by secondary learning from mistakes made when classifying the data parameters.
Published in: 2017 11th International Conference on Signal Processing and Communication Systems (ICSPCS)
Date of Conference: 13-15 December 2017
Date Added to IEEE Xplore: 29 January 2018
ISBN Information: