# Significance of Single-Interval Discrete Attributes: Case Study on Two-Level Discretisation
###### Abstract  
Supervised discretisation is widely considered as far more advantageous than unsupervised transformation of attributes, because it helps to preserve the informative content of a variable, which is useful in classification. After discretisation, based on employed criteria, some attributes can be found irrelevant, and all their values can be represented in a discrete domain by a single interval. In consequence, such attributes are removed from considerations, and no knowledge is mined from them. The paper presents research focused on extended transformations of attribute values, thus combining supervised with unsupervised discretisation strategies. For all variables with single intervals returned from supervised algorithms, the ranges of values were transformed by unsupervised methods with varying numbers of bins. Resulting variants of the data were subjected to selected data mining techniques, and the performance of a group of classifiers was evaluated and compared. The experiments were performed on a stylometric task of authorship attribution. discretisation; pattern recognition; stylometry +
Footnote †: journal: _Applied Sciences_  
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single interval to represent an entire domain for a translated variable, and they then check whether splitting this interval is beneficial. If not, then the defined bin remains the sole representative for all values. As a consequence, the variable has constant nominal value for all samples, thus making it effectively removed from any knowledge discovery process that follows discretisation. Instead of just accepting the verdict that some attributes are judged as useless by supervised discretisation, these variables can be subjected to more complex transformations. The paper reports research works dedicated to not one- but two-level discretisation of features. In the first processing step, the features are discretised through supervised methods. In the second step, for all these attributes which are assigned single intervals, their continuous domains are transformed by unsupervised algorithms. The combination of both approaches enables obtaining meaningful discrete representations for all variables. The experiments were executed within stylometry, the domain focused on characteristics of writing styles [7], where the task of authorship attribution is considered of paramount importance [8]. In the training phase for known authors, stylistic profiles are constructed, which are then measured against samples of writing of unknown or questioned authorship to either confirm or deny it. Profiles are obtained by referring to linguistic descriptors [9], which are most often continuous [10]. Building a profile is not trivial, as authors should be recognised regardless of a particular topic they write about. Knowledge discovery means mining stylometric features and removing any of them from considerations as a consequence of discretisation could be ill afforded. Therefore, any transformation that aims at preserving any available informative content of input features, however small or insignificant it may seem, is welcome, as it may help in recognition. Treating authorship attribution as a supervised learning problem [11], several inducers were applied to the stylometric data to study the influence of discretisation transformations on pattern recognition. The classifiers could operate on both continuous and nominal valued features [12]. For all the classification systems, their performance was observed in the context of discretisation of the input space and the characteristics of such processing. Taking the results from supervised discretisation at face value implied that not only details of attributes' values but also entire domains can end up being disregarded. The experiments showed that it could mean ignoring some cases.