From ambiguity and sensitivity to transparency and contextuality : a research journey to explore error-sensitive value patterns in data classification

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“𝘌𝘳𝘳𝘰𝘳 𝘪𝘴 𝘯𝘰𝘵 𝘢 𝘧𝘢𝘶𝘭𝘵 𝘰𝘧 𝘰𝘶𝘳 𝘬𝘯𝘰𝘸𝘭𝘦𝘥𝘨𝘦, 𝘣𝘶𝘵 𝘢 𝘮𝘪𝘴𝘵𝘢𝘬𝘦 𝘰𝘧 𝘰𝘶𝘳 𝘫𝘶𝘥𝘨𝘮𝘦𝘯𝘵 𝘨𝘪𝘷𝘪𝘯𝘨 𝘢𝘴𝘴𝘦𝘯𝘵 𝘵𝘰 𝘵𝘩𝘢𝘵 𝘸𝘩𝘪𝘤𝘩 𝘪𝘴 𝘯𝘰𝘵 𝘵𝘳𝘶𝘦”. This statement by John Locke, an English philosopher and medical researcher in the 1680s, is still relevant today, and this scope of error can be expanded from knowledge and judgement to result and process in terms of data analysis, to treat errors as a part of the knowledge to learn from rather than to simply eliminate. In the research area of data mining and classification, errors are inevitable due to various factors such as sampling and computation restriction, and measurement and assumption limitations. To address this issue, one approach is to tackle errors head-on, to focus on refining the mining and classification processes by way of theory and algorithm enhancement to reduce errors, and it has been favored by researchers because the research results can be verified directly and clearly. Another approach is to focus on the examination of errors together with the data closely to explore the further understanding of different aspects of the data, especially on attributes and value patterns which may be more sensitive to errors to help identify and reduce errors in a retrospective and indirect way. This research has taken up the latter and less favorable approach to learn from errors rather than simply eliminating them, to examine the potential correlation between the classification results and the specific characteristics of attributes and value patterns, such as value pattern ambiguity, error risk sensitivity and multi-factor contextuality, to help enhance understanding of the errors and data in terms of correlation and context between various data elements for the goal of knowledge discovery as well as error investigation and reduction, not just for researchers, but more importantly, for the stakeholders of the data. This research can be considered a four-stage journey to explore the ambiguity, sensitivity, transparency and contextuality aspects of value patterns from a philosophical and practical perspective, and the research work conducted in each stage of the journey is accompanied by the development of a new error pattern evaluation model to verify the results in a progressive and systematic way. It is all about exploring and gaining further understanding on errors and data from different perspectives and sharing the developments and findings with the aim of generating more interest and motivation for further research into data and correlated factors, internally and externally, transparently and contextually, for the benefit of knowledge discovery.
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