A selection mechanism using multi-criteria evaluation and hierarhical classifying tree for resume data processing

Abstract

The paper considers a problem of optimal feature selection for resume data processing by means of combining multi-criteria evaluation technique and hierarhical classifying trees technology what makes it possible to build a selection mechanism without necessity to collect data for the learning purposes of real applicants. Instead, the learning data are generated by means of the technique used in a full factorial experiment with quite a restricted number of samples. The suggested approach minimizes the number of the features used in selection the best candidates and does not use the quantitative ratings of candidates replacing them with multi-phases classifying procedure. These peculiarities of the suggested selection mechanism make it more flexible and form a basis for applying it in conditions characterized by vagueness and fuzziness of the applicant data.