Latent Confusion Analysis by Normalized Gamma Construction

Issei Sato, Hisashi Kashima, Hiroshi Nakagawa
Proceedings of the 31st International Conference on Machine Learning, PMLR 32(2):1116-1124, 2014.

Abstract

We developed a flexible framework for modeling the annotation and judgment processes of humans, which we called “normalized gamma construction of a confusion matrix.” This framework enabled us to model three properties: (1) the abilities of humans, (2) a confusion matrix with labeling, and (3) the difficulty with which items are correctly annotated. We also provided the concept of “latent confusion analysis (LCA),” whose main purpose was to analyze the principal confusions behind human annotations and judgments. It is assumed in LCA that confusion matrices are shared between persons, which we called “latent confusions”, in tribute to the “latent topics” of topic modeling. We aim at summarizing the workers’ confusion matrices with the small number of latent principal confusion matrices because many personal confusion matrices is difficult to analyze. We used LCA to analyze latent confusions regarding the effects of radioactivity on fish and shellfish following the Fukushima Daiichi nuclear disaster in 2011.

Cite this Paper

BibTeX
@InProceedings{pmlr-v32-satob14, title = {Latent Confusion Analysis by Normalized Gamma Construction}, author = {Sato, Issei and Kashima, Hisashi and Nakagawa, Hiroshi}, booktitle = {Proceedings of the 31st International Conference on Machine Learning}, pages = {1116--1124}, year = {2014}, editor = {Xing, Eric P. and Jebara, Tony}, volume = {32}, number = {2}, series = {Proceedings of Machine Learning Research}, address = {Bejing, China}, month = {22--24 Jun}, publisher = {PMLR}, pdf = {http://proceedings.mlr.press/v32/satob14.pdf}, url = {https://proceedings.mlr.press/v32/satob14.html}, abstract = {We developed a flexible framework for modeling the annotation and judgment processes of humans, which we called “normalized gamma construction of a confusion matrix.” This framework enabled us to model three properties: (1) the abilities of humans, (2) a confusion matrix with labeling, and (3) the difficulty with which items are correctly annotated. We also provided the concept of “latent confusion analysis (LCA),” whose main purpose was to analyze the principal confusions behind human annotations and judgments. It is assumed in LCA that confusion matrices are shared between persons, which we called “latent confusions”, in tribute to the “latent topics” of topic modeling. We aim at summarizing the workers’ confusion matrices with the small number of latent principal confusion matrices because many personal confusion matrices is difficult to analyze. We used LCA to analyze latent confusions regarding the effects of radioactivity on fish and shellfish following the Fukushima Daiichi nuclear disaster in 2011.} }
Endnote
%0 Conference Paper %T Latent Confusion Analysis by Normalized Gamma Construction %A Issei Sato %A Hisashi Kashima %A Hiroshi Nakagawa %B Proceedings of the 31st International Conference on Machine Learning %C Proceedings of Machine Learning Research %D 2014 %E Eric P. Xing %E Tony Jebara %F pmlr-v32-satob14 %I PMLR %P 1116--1124 %U https://proceedings.mlr.press/v32/satob14.html %V 32 %N 2 %X We developed a flexible framework for modeling the annotation and judgment processes of humans, which we called “normalized gamma construction of a confusion matrix.” This framework enabled us to model three properties: (1) the abilities of humans, (2) a confusion matrix with labeling, and (3) the difficulty with which items are correctly annotated. We also provided the concept of “latent confusion analysis (LCA),” whose main purpose was to analyze the principal confusions behind human annotations and judgments. It is assumed in LCA that confusion matrices are shared between persons, which we called “latent confusions”, in tribute to the “latent topics” of topic modeling. We aim at summarizing the workers’ confusion matrices with the small number of latent principal confusion matrices because many personal confusion matrices is difficult to analyze. We used LCA to analyze latent confusions regarding the effects of radioactivity on fish and shellfish following the Fukushima Daiichi nuclear disaster in 2011.
RIS
TY - CPAPER TI - Latent Confusion Analysis by Normalized Gamma Construction AU - Issei Sato AU - Hisashi Kashima AU - Hiroshi Nakagawa BT - Proceedings of the 31st International Conference on Machine Learning DA - 2014/06/18 ED - Eric P. Xing ED - Tony Jebara ID - pmlr-v32-satob14 PB - PMLR DP - Proceedings of Machine Learning Research VL - 32 IS - 2 SP - 1116 EP - 1124 L1 - http://proceedings.mlr.press/v32/satob14.pdf UR - https://proceedings.mlr.press/v32/satob14.html AB - We developed a flexible framework for modeling the annotation and judgment processes of humans, which we called “normalized gamma construction of a confusion matrix.” This framework enabled us to model three properties: (1) the abilities of humans, (2) a confusion matrix with labeling, and (3) the difficulty with which items are correctly annotated. We also provided the concept of “latent confusion analysis (LCA),” whose main purpose was to analyze the principal confusions behind human annotations and judgments. It is assumed in LCA that confusion matrices are shared between persons, which we called “latent confusions”, in tribute to the “latent topics” of topic modeling. We aim at summarizing the workers’ confusion matrices with the small number of latent principal confusion matrices because many personal confusion matrices is difficult to analyze. We used LCA to analyze latent confusions regarding the effects of radioactivity on fish and shellfish following the Fukushima Daiichi nuclear disaster in 2011. ER -
APA
Sato, I., Kashima, H. & Nakagawa, H.. (2014). Latent Confusion Analysis by Normalized Gamma Construction. Proceedings of the 31st International Conference on Machine Learning, in Proceedings of Machine Learning Research 32(2):1116-1124 Available from https://proceedings.mlr.press/v32/satob14.html.

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