Immediate Assessment of Batch Classification Quality

Batch classification is used in selection settings where the data from a number of applicants are processed in order to decide which applicants will be assigned to a number of different vacant jobs. Batch classification, in opposition to sequential systems, processes the data of a whole group of applicants simultaneously. This is appropriate in settings where the enlistment is organized in groups, such as annual recruitments. Modern batch classification systems are generally composed of two major elements. In the first element it is attempted to quantify the value of assigning a specific person to a specific job or a certain type of jobs. In the military, similar jobs are often labeled as Military Occupation Specialties MOS or as trades. The quantified values are called payoff-values and can be computed in several ways. Multiple linear regressions MLR are widely used. In MLR models, the payoffs usually are predicted performance scores on an external criterion that was used as dependent variable when designing the MLR model. Another method to produce payoff-values is the Subject Matter Experts-method SME. In this method, subject matter experts are asked to give a specific weight to the selection variables for each MOS or trade. The payoffs can then be calculated as weighted sums. Artificial Neural Networks are also promising tools to generate payoff-values. The payoffs are computed for all person-job combinations and usually arranged in a payoff-matrix with the applicants as rows and the jobs as columns. The matrix is then squared by adding dummy-jobs. When the payoff-matrix is ready, the second major element of the classification model is used. Since the matrix was squared it is possible to link each applicant to a job a real one or a dummy and each job to an applicant. That can be done by means of an algorithm that maximizes the sum of the payoff values identified by linking a person to a job.