Improved Inductive Learning Approach -5 (IILA-5) in Distributed System
Keywords:
MVI (Missing Value Imputation), ILA-1 (Inductive Learning Approach-1), ILA-2, ILA-3, ILA-4, ILA-5, FastILAAbstract
The job recommender system is a proficient data-driven application that utilizes inductive learning to create a comprehensive set of classifying rules that effectively matches suitable jobs and skills to both candidates and recruiters. The system faces difficulties in real-world scenarios because the data it deals with frequently contains noise, insufficiency, activeness, unwanted attributes, continuous variables, and missing values. As a result, generating accurate rules from such inconsistent datasets becomes a difficult task. To address these issues, the system employs the novel Inductive Learning Approach-5 (ILA-5) algorithm. ILA-5 is specifically designed to address these complexities by removing unnecessary and irrelevant rules while improving prediction accuracy on unseen training data. The algorithm employs an iterative approach, actively exploring rules that improve training sample arrangement. When a set of rules is generated, the system labels the corresponding training data samples. Following cycles, the algorithm efficiently rejects rules that do not contribute significantly to the overall accuracy of the recommendations. The ILA-5 algorithm's effectiveness has been rigorously tested on two distinct datasets, namely CareerBuilder and Niti Aayog's dataset. The astounding results show its ability to generate rules with an impressive 91% accuracy for job recommendation. Furthermore, the algorithm has excellent scalability, allowing it to handle large datasets without sacrificing efficiency. Overall, the job recommender system with ILA-5 is a cutting-edge solution that provides powerful, accurate, and scalable job recommendations, benefiting both job seekers and recruiters in the ever-evolving job market.
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