Weighting of features by sequential selection

Constructing a set with characteristic features for supervised classification is a task which can be considered as preliminary for the intended purpose, just a step to take on the way, yet with its significance and bearing on the outcome, the level of difficulty and computational costs involved, the problem has evolved in time to constitute by itself a field of intense study. We can use statistics, available expert domain knowledge, specialised procedures, analyse the set of all accessible features and reduce them backward, we can examine them one by one and select them forward. The process of sequential selection can be conditioned by the performance of a classification system, while exploiting a wrapper model, and the observations with respect to selected variables can result in assignment of weights and ranking. The chapter illustrates weighting of features with the procedures of sequential backward and forward selection for rule and connectionist classifiers employed in the stylometric task of authorship attribution.