Stacked KNN with hard voting predictive approach to assist hiring process in IT organizations

Abstract

Effective candidates screening is critical for any IT firm as it impacts the future growth and productivity of that firm. Currently majority of these firms follow a manual approach of hiring employees which is more prone to errors and time consuming. Prime purpose of the research is to develop an intelligent predictive model to decide upon candidate’s suitability for an applied It based job. A sample size of 13,168 instances with 19 attributes of job seekers data are used for study. An ensemble predictive meta model approach is presented in the research where a stacked KNN (K-Nearest Neighbours) algorithm combined with hard voting approach is employed. The idea is to use a single unified predictive model in place of separate classification models by considering the predicted class with maximum votes against each class label. The proposed approach comprises two functional modules. ‘Stacked KNN Learner Module’ involves the use of five variants of KNN which include 1-NN, 3-NN, 5-NN, 7-NN and 9-NN. Individual prediction of the job seeker data from these variants of KNN algorithm are input to the ‘Hard Voting Ensemble Predictive unit’ which eventually aggregates majority of predicted class votes determined for each class label to generate the final output predicted class label on basis of maximum votes. The developed ensemble meta model is successfully implemented using python programming language and its performance evaluation is done using various metrics. The implemented model generated a 96.96% prediction accuracy rate. The specificity, sensitivity and f-score value recorded was 96.36%, 96.06% and 96.26% respectively. Mean Absolute Error (MAE) and Root mean Square Error (RMSE) value observed with our proposed meta model was 0.0048 and 0.0102 respectively. A comparative analysis of the proposed model was done by varying the data sample size and it gave a consistent performance throughout. It was observed that proposed ensemble model gave an impressive 96.96% mean accuracy rate and the overall performance of other variants was consistent. Performance of proposed meta model was also compared with some existing techniques like C.45, CART and K-Means. It outperformed other techniques in terms of accurately predicting the class label of candidates applying for job. This ensemble meta model can be of great help for growing IT firms and assist the organization unit in hiring right and deserving candidates in future. Proposed work can act as a decision making framework in multi sector units with enormous work force to streamline performance appraisal process thereby enabling in hiring right people for right job.

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