Hamidah Jantan

Classification for talent management using Decision Tree Induction techniques

Classification is one of the tasks in Data mining. Nowadays, there are many classification techniques being used to solve classification problems such as Neural Network, Genetic Algorithm, Bayesian and others. In this article, we attempt to present a study on how talent management can be implemented using Decision Tree Induction techniques. By using this approach, talent performance can be predicted using past experience knowledge discovered from the existing database. In the experimental phase, we use selected classification algorithms from Decision tree techniques to propose suitable classifier for the dataset. As a result, the C4.5 classifier algorithm shows the highest accuracy of model for the dataset. Consequently, the possible talent rules are generated based on C4.5 classifier especially for the talent forecasting purposes.

Potential intelligent techniques in Human Resource Decision Support System (HR DSS)

Human resource decisions are subject to limitations, because they always depend on human knowledge, judgement and preference. Decision support applications can be used to provide fair and consistent decisions, besides to improve the effectiveness of decision making processes. An intelligent decision support system (IDSS) is developed to assist decision makers in high level phases of decision making by integrating human knowledge with modeling tools. In this paper, we described the potential to implement the IDSS in human resource management (HRM) activities. This study consists of three parts; the first part is to understand the IDSS concepts, applications and related research in human resources decision making application known as HR DSS. The second part is to identify the potential intelligent techniques that can be used in HR DSS application, and the third part is to suggest the HR DSS framework that is related to human resource decisions. Finally, the paper proposed the HR DSS framework and the potential intelligent techniques that can be used to develop the IDSS application in any phases of decision making processes.

Intelligent Techniques for Decision Support System in Human Resource Management

Nowadays, the evolution of information technology applications makes it an absolute obligation on behalf of the decision makers to continuously make the best decisions in the shortest possible time. Decision Support System (DSS) is a technology and application that assists managerial decision makers utilizing data and models to solve semi-structured and unstructured problems (Qian et al., 2004). This chapter discusses general issues on DSS technologies and an idea to apply DSS technologies into Human Resources Management (HRM) field. Recently, the collaboration between DSS technologies and Artificial Intelligent techniques has produced another type of DSS technology known as Active DSS, it is a technology that will take place in the new millennium era (Shim et al., 2002). Active DSS is an outcome of new DSS technologies and also known as a part of Intelligent System applications. Active DSS applications such as Expert System, Knowledge-based System, Adaptive DSS and Intelligent Decision Support System (IDSS) are categorized as part of Intelligent System studies. Expert systems technology, which was a crucial area for enterprise capital in 1985-1990, is now being replaced by the intelligent system applications (Faye et al., 1998). Intelligent systems are developed to fulfill the two main functions. Firstly, to screening, shifting and filtering the increasing overflow of data, information and knowledge. Secondly, as a supporter of an effective and productive decision making that is suitable to the user needs. Intelligent systems can be developed for these purposes; range from self-organizing maps to smart add-on modules to make the use of applications more effective and useful for the users (Shim et al., 2002). Human is important and a very valuable asset for an organization and managed by Human Resource professional. HRM system is an important element in the success of an organization, known as an integrated and interrelated approaches to managing human resources (DeNisi & Griffin, 2005). Activities in HRM involve a lot of unstructured processes such as staffing, training, motivation and maintenance (DeCenZo & Robbins, 2005). Besides that, decision making for unstructured processes in HRM usually depends on human judgment and preference. However, human decisions are subject to the limitation because sometimes people forget the crucial details of the problem, and besides, fairness and

Classification and prediction of academic talent using data mining techniques

In talent management, process to identify a potential talent is among the crucial tasks and need highly attentions from human resource professionals. Nowadays, data mining (DM) classification and prediction techniques are widely used in various fields. However, this approach has not attracted much interest from people in human resource. In this article, we attempt to determine the potential classification techniques for academic talent forecasting in higher education institutions. Academic talents are considered as valuable human capital which is the required talents can be classified by using past experience knowledge discovered from related databases. As a result, the classification model will be used for academic talent forecasting. In the experimental phase, we have used selected DM classification techniques. The potential technique is suggested based on the accuracy of classification model generated by that technique. Finally, the results illustrate there are some issues and challenges rise in this study, especially to acquire a good classification model.

Data Mining Classification Techniques for Human Talent Forecasting

In knowledge management process, data mining technique can be used to extract and discover the valuable and meaningful knowledge from a large amount of data. Nowadays, data mining has given a great deal of concern and attention in the information industry and in society as a whole. This technique is an approach that is currently receiving great attention in data analysis and it has been recognized as a newly emerging analysis tool (Osei-Bryson, 2010; Park, 2001; Sinha, 2008; Tso & Yau, 2007; Wan, 2009; Zanakis, 2005; Zhuang et al., 2009). Additionally, among the major tasks in data mining are classification and prediction; concept description; rule association; cluster analysis; outlier analysis; trend and evaluation analysis; statistical analysis and others. Classification and prediction tasks are among the popular tasks in data mining; and widely used in many areas especially for trend analysis and future planning. In fact, classification technique is supervised learning, which is the class level or prediction target is already known. As a result, the classification model which is represented through rules structures will be constructed in the classification process. In this case, the constructed model will be representing the precious knowledge and it can be used for future planning. There are many areas which adapted this approach to solve their problems such as in finance, medical, marketing, stock, telecommunication, manufacturing, health care, customer relationship and etc. However, the data mining application has not attracted much attention from people in Human Resource (HR) field (Chien & Chen, 2008; Ranjan, 2008). Besides that, in our previous study, most of the prediction applications are used to predict stock, demand, rate, risk, event and others; but there are quite limited studies on human prediction. In addition prediction applications are mainly developed in business and industrious fields; and quite restricted studies involved human talent in an organization (Jantan et al., 2009). HR data can provide a rich resource for knowledge discovery and for decision support system development. Recently, an organization has to struggle effectively in term of cost, quality, service or innovation. All these depend on having enough right people with the right skills, employed

Talent knowledge acquisition using data mining classification techniques

Data Mining classification task is categorized as a part of knowledge acquisition process, which can be implemented through the analysis procedure in related databases. In this study, we aimed to employ this technique to perform talent knowledge acquisition process in Human Resource (HR) by using talent databases. In HR, among the challenges of HR professionals is to manage organizations talents, especially to ensure the right person assign to the right job at the right time. In this case, knowledge discovered from talent knowledge acquisition process can be used by professionals in HR to handle various tasks in talent management. In this article, we present an experimental study to identify the potential data mining classification technique for talent knowledge acquisition. Talent knowledge discovered from related databases can be used to classify the appropriate talent among employees. In experimental phase, we used selected classification algorithms in order to propose the suitable classifier from talent datasets. As a result, the C4.5 classifier algorithm from decision tree family is recommended as a suitable classifier for the datasets. Classification model performed by this classifier can be used in talent management especially for talent classification or prediction.

Potential Data Mining Classification Techniques for Academic Talent Forecasting

Classification and prediction are among the major techniques in Data mining and widely used in various fields. In this article we present a study on how some talent management problems can be solved using classification and prediction techniques in Data mining. By using this approach, the talent performance can be predicted by using past experience knowledge discovered from the existing database. In the experimental phase, we have used selected classification and prediction techniques to propose the appropriate techniques from our training dataset. An example is used to demonstrate the feasibility of the suggested classification techniques using academician performance data. Thus, by using the experiments results, we suggest the potential classification techniques for academic talent forecasting.

Intelligent DSS for talent management: a proposed architecture using knowledge discovery approach

Decision making tasks are subject to limitation and also depends on human knowledge, experiences, judgments and preferences. In this case, Intelligent Decision System (IDS) technologies can be used to provide realistic and consistent decisions, besides to improve the effectiveness of decision making processes. Intelligent Decision Support System (IDSS) is a contributory of IDS technology to assist decision makers in high level phases of decision making by integrating human knowledge with modeling tools. Nowadays, data mining (DM) techniques is also can be used to support Knowledge Management (KM) tasks especially for knowledge discovery and knowledge engineering. DM is emerging data analysis tool and widely used in order to produce valuable knowledge for decision making as knowledge modeling task. In Human Resource (HR), managing talent is among the challenges of HR professionals which can be handle by using IDSS and data mining technologies. For that reason, in this article, we discussed the potential to uses IDSS approach for talent management using DM techniques by proposing IDSS architecture and a case study on talent classification. This study consists of three parts; the first part is to know the previous works on IDSS, talent management and DM. The second part is discussion on proposed IDSS architecture for talent management. Finally, the third part is a case study on the use of DM classification method for talent forecasting, especially for employees job promotion.

Fuzzy Analytic Hierarchy Process for Multi-criteria Academic Successor Selection

The selection of leader for academic position in Higher Learning Institution (HLI) involves Multi Criteria Decision Making (MCDM) process. The decision making becomes complicated once it deal with the multiple candidates, multiple conflicting criteria and imprecise parameters. In addition, the uncertainty and vagueness of the experts’ opinion is considered as the prominent characteristics of the problem. This paper proposed an academic multi-criteria succession selection approach using Fuzzy Analytic Hierarchy Process (FAHP). This study consists of three phase’s i.e. academic multi-criteria model development, data collection, successor selection using FAHP and result analysis. The dataset was collected from several assessors in selected HLI based on the proposed multi-criteria model for academic leader. The aims of this study is to determine the best candidate for academic position based on the higher weight obtained by the candidate. The potential academic successor was obtained after analyzing different dataset for the same candidate that evaluated by different assessors. In future, this study attempts to optimize the result in selection process by incorporating with soft computing method such bio-inspired method such as Particle Swarm Optimization (PSO), Ant Colony Optimization (ACO), Artificial Bee Colony (ABC) and etc.

Pattern Search Based on Particle Swarm Optimization Technique for Block Matching Motion Estimation Algorithm

Block matching algorithm is a popular technique in developing video coding applications that is used to reduce the computational complexity of motion estimation (ME) algorithm. In a video encoder, efficient implementation of ME is required that affect the final result in any applications. Searching pattern is one of the factors in developing motion estimation algorithm that could provide good performance. A new enhanced algorithm using a pattern based particle swarm optimization (PSO) has been proposed for obtaining least number of computations and to give better estimation accuracy. Due to the center biased nature of the videos, the proposed algorithm approach uses an initial pattern to speed up the convergence of the algorithm. The results have proved that improvements over Hexagon base Search could achieved with 7.82%–17.57% of computations cost reduction without much value of degradation of image quality. This work could be improved by using other variant of PSO or other potential meta-heuristic algorithms to provide the better performances in both aspects.