Title: Towards Applying Support Vector Machine Algorithm in Employee Achievement Classification

Year of Publication: Nov - 2014
Page Numbers: 12-21
Authors: Hamidah Jantan, Norazmah Mat Yusoff, Mohamad Rozuan Noh
Conference Name: The International Conference on Data Mining, Internet Computing, and Big Data (BigData2014)
- Malaysia


Human capital is the key factor to maintain the competitiveness of an organization by having enough right people with the right skills. In technology advancement, machine learning technique can be used in order to identify the right employee for the right task by classifying their performance achievement. Support Vector Machine (SVM) is a powerful supervised machine learning technique for classification because it uses kernel trick with the ability to build expert knowledge for the problem via kernel engineering process. In this study, Sequential Minimal Optimization (SMO) algorithm from SVM technique is the chosen method due to its capability to solve most of convex optimization problem. This study consists of four phases; data collection, data preparation, model development and model evaluation. In the experimental phase, selected academician performance achievement data in Malaysian Higher Institution have been used as the training dataset based on 10-fold cross validation. Several experiments were carried out by using different set of training and testing datasets to evaluate the accuracy of the model. As a result, the accuracy of the proposed model is considered acceptable and needs further enhancement. For future work, to enhance the accuracy of the proposed model, a comparative study should be conducted using other SVM algorithms such as Grid Search and Gabriel graph algorithms that focus on reducing the size of a training set.