Abstract
Due to the massive influence in the use of prediction models in different sectors of society, many researchers have employed hybrid algorithms to increase the accuracy level of the prediction model. The literature suggests that the use of Genetic Algorithms (GAs) can sufficiently improve the performance of other prediction models; thus, this study. This paper introduced a new avenue of prediction integrating GA with the novel Inversed Bi-segmented Average Crossover (IBAX) operator paired with rank-based selection function to the KNN algorithm. The 70% of data from 597 records of student-respondents in the evaluation of the faculty instructional performance from the four State Universities and Colleges (SUC) in Caraga Region, Philippines were used as training set while the 30% was used for testing. The simulation result showed that the use of the proposed prediction model with the integration of the modified GA outperformed the KNN prediction model where GA with average crossover and roulette wheel selection function was used. The KNN where k value is three (3) was identified to be the optimal model for prediction with the 95.53% prediction accuracy compared to KNN with 1, 5, and 7 k values.
Highlights
Data Mining (DM) is the process of extracting implicit information or knowledge from databases [1]-[3], that is drawn from the field of statistics [4] which uses mathematical and machine learning techniques and algorithms [5]
Knowledge Discovery in Databases (KDD) which is coined to data mining [6], represents the generally observed process in knowledge discovery where knowledge is the result of the data-driven discovery while data mining being the observed step in the process for efficiently automated discovery, employs diverse approaches of DM analysis [7]
The hybrid Genetic Algorithms (GAs)-selforganizing map (SOM) yielded 91.95% prediction accuracy compared with the 84.96% prediction of the standalone SOM algorithm
Summary
Data Mining (DM) is the process of extracting implicit information or knowledge from databases [1]-[3], that is drawn from the field of statistics [4] which uses mathematical and machine learning techniques and algorithms [5]. The field of data mining has become standard practice in various disciplines such as business, finance, and marketing allowing to inadvertently impact social sciences and humanities in general [8]. DM is promising for researches applied in engineering, biomedical sciences, medical systems, web, sports, and shared market because of the accessibility to various vast datasets [13], [14].
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callMore From: International Journal of Advanced Computer Science and Applications
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
