Data Mining Classification Techniques for Human Talent Forecasting

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

doi:10.5772/14007

Cited by 14 publications

(5 citation statements)

References 21 publications

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“…Therefore, the classification of instance can be as true (True positive (TP) or True negative (TN)) or false (False positive (FP) or False negative (FN)). According to these four cases, the accuracy, sensitivity, and specificity are defined in Table V [16], [17]. In experimental analysis, we also consider other metrics summarized in Table V.…”

Section: Accuracy Of the Proposed Algorithmmentioning

confidence: 99%

“…Typical classifiers used in the second step need numerical attributes for the classification, but EEG signal is represented as a function of time which cannot be directly classified. Therefore, this signal should be transformed into samples of numerical attributes in the step of signal preprocessing [15], [16], [17], which is also known as the step of the preliminary transformation. EEG signal is preprocessed to remove noise and extract useful information needed for the next step of classification.…”

Section: Introductionmentioning

confidence: 99%

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Review of Methods for EEG Signal Classification and Development of New Fuzzy Classification-Based Approach

2020

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The analysis of EEG signal is a relevant problem in health informatics, and its development can help in detection of epileptic's seizures. The diagnosis is based on classification of EEG signal. Different methods and algorithms for classification of EEG signals with an accepted level of reliability and accuracy have been developed over years. All these methods have two steps that are signal preprocessing and classification. The goal of the preprocessing step is removing noise and reduction of the initial signal dimensionality. The signal dimensionality reduction is required by classification methods, but its result is a loss of small information before the classification. In this paper, an approach for EEG signal classification that takes this loss of information into account is considered. The novelty of the considered approach is usage of fuzzy classifier in the classification step. This classifier allows taking uncertainty of initial data into account, which is caused by loss of some information during dimensionality reduction of initial signal. However, application of fuzzy classifier needs modification of the preprocessing step because it requires data in fuzzy form. Therefore, fuzzification procedure is added to the preprocessing step. In this paper, Fuzzy Decision Tree (FDT) is used as the fuzzy classifier for the epileptic's seizure detection. Its application allows achieving 99.5% accuracy of the classification of epileptic's seizure. The comparison with other studies shows that FDT is very effective for task of epileptic's seizure detection.

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Section: Accuracy Of the Proposed Algorithmmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Review of Methods for EEG Signal Classification and Development of New Fuzzy Classification-Based Approach

2020

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“…Massaro et al [12] developed some tools in a project related to industry research on business intelligence, they used data mining tools like Weka, Rapid Miner, and KNIME workflows, and some big data techniques, they checked a good performance for all the outputs of the algorithms, they also developed a model based on big data connection, multi-attribute analysis and neural network workflow that can predict E-commerce sales with a convenient performance, Some of these inputs of this created model are the outputs of other data mining tools like social sentiment analysis. Jantan et al [13] used data mining classification algorithms to detect the "talent employed" in the manufacturing environment. The results for these algorithms showed the highest accuracy of the used model is C4.5 (95.14%, 99.90%, and 90.54%).…”

Section: Introductionmentioning

confidence: 99%

Knowledge discovery in manufacturing datasets using data mining techniques to improve business performance

Shaaban

Khafagy

Elmasry

et al. 2022

IJEECS

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Recently due <span>to the explosion in the data field, there is a great interest in the data science areas such as big data, artificial intelligence, data mining, and machine learning. Knowledge gives control and power in numerous manufacturing areas. Companies, factories, and all organizations owners aim to benefit from their huge; recorded data that increases and expands very quickly to improve their business and improve the quality of their products. In this research paper, the knowledge discovery in databases (KDD) technique has been followed, “association rules” algorithms “Apriori algorithm”, and “chi-square automatic interaction detection (CHAID) analysis tree” have been applied on real datasets belonging to (Emisal factory). This factory annually loses tons of production due to the breakdowns that occur daily inside the factory, which leads to a loss of profit. After analyzing and understanding the factory product processes, we found some breakdowns occur a lot of days during the product lifecycle, these breakdowns affect badly on the production lifecycle which led to a decrease in sales. So, we have mined the data and used the mentioned methods above to build a predictive model that will predict the breakdown types and help the factory owner to manage the breakdowns risks by taking accurate actions before the breakdowns happen.</span>

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“…The process of talent assessment in HR field involves highly on human decisions by managing employee's advancement through promotion exercises, those tasks are very subjective, uncertain and difficult [4]. In HR field, employee's assessment process is a way for an employee to enhance his/her career path development.…”

Section: Introductionmentioning

confidence: 99%

Artificial Immune-based Algorithm for Academic Leadership Assessment

Jantan¹,

Azemi²,

Othman³

2019

IJACSA

Self Cite

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Artificial Immune-based algorithm is inspired by the biological immune system as computational intelligence approach in data analysis. Negative selection algorithm is derived from immune-based algorithm's family that used to recognize the pattern's changes perform by the gene detectors in complementary state. Due to the self-recognition ability, this algorithm is widely used to recognize the abnormal data or nonself especially for fault diagnosis, pattern recognition, network security etc. In this study, the self-recognition performance proposed by the negative selection algorithm been considered as a potential technique in classifying employee's competency. Assessing the employee's performance in organization is an important task for human resource management people to identify the right candidate in job promotion assessment. Thus, this study attempts to propose an immune-based model in assessing academic leadership performance. There are three phases involved in experimental phase i.e. data acquisition and preparation; model development; and analysis and evaluation. The data consists of academic leadership proficiency was prepared as data-set for learning and detection processes. Several experiments were conducted using cross validation process on different model to identify the most accurate model. Therefore, the accuracy of NS classifier is considered acceptable enough for this academic leadership assessment case study. For enhancement, other immune-based algorithm or bio-inspired algorithms, such as genetic algorithm, particle swam optimization, ant colony optimization would also be considered as a potential algorithm for performance assessment.

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Data Mining Classification Techniques for Human Talent Forecasting

Cited by 14 publications

References 21 publications

Review of Methods for EEG Signal Classification and Development of New Fuzzy Classification-Based Approach

Review of Methods for EEG Signal Classification and Development of New Fuzzy Classification-Based Approach

Knowledge discovery in manufacturing datasets using data mining techniques to improve business performance

Artificial Immune-based Algorithm for Academic Leadership Assessment

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