The choice of particular variables for construction of a set of characteristic features relevant to classification can be executed in a kind of external process with respect to a classification system employed in pattern recognition, it can depend on the performance of such system, or it can involve some inherent mechanism, build-in in the system. The three types of approaches correspond to three categories of methodologies typically exploited in feature selection and reduction: filters, wrappers, and embedded solutions, respectively. They are used when domain knowledge is unavailable or insufficient for an informed choice, or in order to support this expert knowledge to achieve higher efficiency, enhanced classification, or reduced sizes of classifiers. The chapter illustrates the combinations of the three approaches with the aim of feature evaluation, for binary classification with balanced, for the task of authorship attribution that belongs with stylometric analysis of texts.

Feature Evaluation by Filter, Wrapper, and Embedded Approaches

The paper describes research on ways of datasets discretization, when test datasets are used for evaluation of a classifier. Three different approaches of processing for training and test datasets are presented: “independent”—where discretization is performed separately for both sets assuming that the same algorithm parameters are used; “glued”—where both sets are concatenated, discretized, and resulting set is separated to obtain training and test sets, and finally “test on learn”—where test dataset is discretized using ranges obtained from learning data. All methods have been investigated and tested in authorship attribution domain using Naive Bayes classifier.

On Approaches to Discretization of Datasets Used for Evaluation of Decision Systems

The paper presents research on class imbalance in the context of construction of training sets for authorship recognition. In experiments the sets are artificially imbalanced, then balanced by under-sampling and over-sampling. The prepared sets are used in learning of two predictors: connectionist and rule-based, and their performance observed. The tests show that for artificial neural networks in several cases the predictive accuracy is not degraded but in fact improved, while one rule classifier is highly sensitive to class balance as it never performs better than for the original balanced set and in many cases worse.

The Class Imbalance Problem in Construction of Training Datasets for Authorship Attribution

Rule classifiers enhance understanding of data and enable to represent learned knowledge in an explicit structural form. To further improve this understanding and generalisation properties of predictors, the process of selection of decision rules can be executed. The paper presents a post-processing approach to this task. In a proposed research framework there were exploited rankings of attributes the rules refer to, thus transferring the established importance of features to the rules based on them. The attributes were weighted by selected statistical measures and machine learning algorithms. The rule classifiers were constructed in Dominance-Based Rough Set Approach and employed in the domain of stylometry for the task of authorship attribution.

Selection of decision rules based on attribute ranking

RELIEF-based Selection of Decision Rules

Decision algorithms correspond to the rule-based approach to classification and pattern recognition problems. While to shorten the processing time we need as few constituent decision rules as possible, when their number is too low it may lead to a poor performance of the classifier. The decision rules can be found by providing the minimal cover of the training samples, by calculating rules with some genetic algorithms, by the exhaustive search for all rules. This last option offers the widest choice of rules, which enables tailoring the final algorithm to the task at hand, yet this is achieved by the additional cost of rule selection process. Usually there are assumed some measures indicating the quality of individual decision rules. The paper presents a different procedure, which is closer to feature reduction. In the first step there are selected condition attributes that are discarded, then the rules that contain conditions on these attributes are removed from the algorithm. The classifier performance is observed in the domain of computational stylistics, which is a study on characteristics of writing styles.

Rule-Based approach to computational stylistics

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Rule-Based approach to computational stylistics