Classification for talent management using Decision Tree Induction techniques

Jantan, Hamidah; Hamdan, Abdul Razak; Othman, Zulaiha Ali

doi:10.1109/dmo.2009.5341916

Cited by 12 publications

(8 citation statements)

References 12 publications

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“…In the second phase, test data were used to estimate the accuracy of discovered rules. If the accuracy is considered acceptable, the rules can be applied to the classification of new or unseen data (Jantan et al , 2009). The reason of using two sets of data in the classification process was to avoid overfitting.…”

Section: Methodsmentioning

confidence: 99%

Which types of leadership styles do followers prefer? A decision tree approach

Salehzadeh¹

2017

IJEM

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Purpose The purpose of this paper is to propose a new method to find the appropriate leadership styles based on the followers’ preferences using the decision tree technique. Design/methodology/approach Statistical population includes the students of the University of Isfahan. In total, 750 questionnaires were distributed; out of which, 680 accurate questionnaires were used for data analysis. This research in five steps proposes a framework to extract the information about the leadership styles that followers like and this method is examined for situational leadership theory’s styles among the university students. Findings Based on the results of the decision tree model, 27 rules were discovered. Also, the findings imply that, most undergraduate, postgraduate, and PhD students prefer directing, coaching, and supporting styles, respectively. Originality/value The results of this research help leaders to find a better understanding about the followers’ preferences.

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Section: Methodsmentioning

confidence: 99%

Which types of leadership styles do followers prefer? A decision tree approach

Salehzadeh¹

2017

IJEM

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“…Data mining is a machine learning approach and includes many tasks such as: concept description; cluster analysis; classification and prediction; trend and evaluation analysis; outlier analysis; statistical analysis and others. The most important tasks in data mining are classification and prediction techniques [1]. The classification methods are known as supervised learning, where the classification target and the class level are already recognized.…”

Section: 10 Introductionmentioning

confidence: 99%

Comparison of Decision Tree Methods in Classification of Researcher’s Cognitive styles in Academic Environment

2015

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In today's internet world, providing feedbacks to users based on what they need and their knowledge is essential. Classification is one of the data mining methods used to mine large data. There are several classification techniques used to solve classification problems. In this article, classification techniques are used to classify researchers as "Expert" and "Novice" based on cognitive styles factors in academic settings using several Decision Tree techniques. Decision Tree is the suitable technique to choose for classification in order to categorize researchers as "Expert" and "Novice" because it produces high accuracy. Environment Waikato Knowledge Analysis (WEKA) is an open source tool used for classification. Using WEKA, the Random Forest technique was selected as the best method because it provides accuracy of 92.72728. Based on these studies, most researchers have a better knowledge of their own domain and their problems and show more competencies in their information seeking behavior compared to novice researchers. This is because the "experts" have a clear understanding of their research problems and is more efficient in information searching activities. Classification techniques are implemented as a digital library search engine because it can help researchers to have the best response according to their demand.

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“…There are various classification approaches proposed by the researchers in machine learning, statistics, and pattern recognition [8]. This section reviews the different data mining techniques that are being used for the classification and prediction and the prior work done on the respective topic.…”

Section: Literature Reviewmentioning

confidence: 99%

“…This section reviews the different data mining techniques that are being used for the classification and prediction and the prior work done on the respective topic. The techniques that are reviewed are Naïve Bayes, KNN, C4.5, ID3, and SVM [8]- [10].…”

Section: Literature Reviewmentioning

confidence: 99%

An Experimental Study of Classification Algorithms for Terrorism Prediction

Tolan¹,

Soliman²

2015

IJKE

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Abstract-Terrorist attacks are the biggest challenging problem for the mankind across the world, which need the wholly attention of the researchers, practitioners to cope up deliberately. To predict the terrorist group which is responsible of attacks and activities using historical data is a complicated task due to the lake of detailed terrorist data. This research based on predicting terrorist groups responsible of attacks in Egypt from year 1970 up to 2013 by using data mining classification technique to compare five base classifiers namely; Naïve Bayes (NB), K-Nearest Neighbour (KNN), Tree Induction (C4.5), Iterative Dichotomiser (ID3), and Support Vector Machine (SVM) depend on real data represented by Global terrorism Database (GTD) from National Consortium for the study of terrorism and Responses of Terrorism (START). The goal of this research is to present two different approaches to handle the missing data as well as provide a detailed comparative study of the used classification algorithms and evaluate the obtained results via two different test options. Experiments are performed on real-life data with the help of WEKA and the final evaluation and conclusion based on four performance measures which showed that SVM, is more accurate than NB and KNN in mode imputation approach, ID3 has the lowest classification accuracy although it performs well in other measures, and in Litwise deletion approach; KNN outperformed the other classifiers in its accuracy, but the overall performance of SVM is acceptable than other classifiers.Index Terms-KDD, precision, recall, terrorist group. I. INTRODUCTIONTerrorist attacks are biggest, challenging, and leading issue in the whole world. It is one of the central points of concentration in all governments. Data mining is popularly known as Knowledge Discovery in Databases (KDD), it is a logical process of discovering new patterns from large data sets involving methods combined with statistics, database systems, support vector machine, artificial intelligence, meta-heuristics, and machine learning. The main goal of data mining is to extract useful, hidden predictive knowledge from large data sets in a human understandable structure and involves database, data management and pre-processing tools, model and interface capabilities, post-processing of discovered structure, visualization and online updating methods for finding hidden patterns, and predictive information that expert may miss because it lies outside their expectations [1], [2]. Data mining and automated data analysis techniques have become used as effective branch of the most important key features for many applications, data mining has a wide number of applications ranging from marketing and advertising of goods, services or products, artificial intelligence research, biological sciences, crime investigations to high-level government intelligence [3]. Recently there has been much concern on using data mining in detecting and investigating unusual patterns, crimes, terrorist activities and preventing the fraudulent...

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Classification for talent management using Decision Tree Induction techniques

Cited by 12 publications

References 12 publications

Which types of leadership styles do followers prefer? A decision tree approach

Which types of leadership styles do followers prefer? A decision tree approach

Comparison of Decision Tree Methods in Classification of Researcher’s Cognitive styles in Academic Environment

An Experimental Study of Classification Algorithms for Terrorism Prediction

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