Cost-Sensitive Decision Tree Induction for Feature Selection and Sequential Minimal Optimisation for Classification: CSattribSelectorC4.5()

Desai, Ankit; Vasa, Jalpesh

doi:10.5958/j.2249-3220.3.2.008

Cited by 2 publications

(1 citation statement)

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“…Numerous particular decision tree algorithms exist. Notable ones include:  C4.5 algorithm [9]  C5.0 algorithm  ID3 algorithm  Chi-squared Automatic Interaction Detector (CHAID). Performs multi-level splits when computing classification trees [10].…”

Section: (B)(ii) Decision Treementioning

confidence: 99%

Foreseeing Employee Attritions using Diverse Data Mining Strategies

Vasa¹,

Masrani²

2019

IJRTE

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“Employee turnover is a noteworthy matter in knowledge-based companies.” On the off chance that employee leaves, they carry with them tacit information, often a source of competitive benefit to the other firms. Keeping in mind the end goal, to stay in the market and retain its employees, an organization requires minimizing employee attrition. This article discusses the employee churn/attrition forecast model using various methods of Machine Learning. Model yields are then scrutinized to outline and experiment the best practices on employee withholding at different stages of the employee’s association with an organization. This work has the potential for outlining better employee retention designs and enhancing employee contentment. This paper incorporates and condenses the capacity to gain from information and give information-driven experiences, choice, and forecasts and thinks about significant machine learning systems that have been utilized to create predictive churn models.

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Section: (B)(ii) Decision Treementioning

confidence: 99%