DocumentCode :
3582699
Title :
Classification of high dimensional Educational Data using Particle Swarm Classification
Author :
Yahya, Anwar Ali ; Osman, Addin
Author_Institution :
Fac. of Comp. Sci. & Inf. Syst., Najran Univ., Najran, Saudi Arabia
fYear :
2014
Firstpage :
34
Lastpage :
41
Abstract :
This paper explores the effectiveness of Particle Swarm Classification technique for tackling a classification problem in an emergent data mining field, called Educational Data Mining. More specifically, it applies Particle Swarm Classification to classify a data set of teachers´ classroom questions into the cognitive levels of Bloom´s taxonomy. Furthermore, the high dimensionality of questions data set enables investigating the effectiveness of particle swarm classification for the classification in high dimensional domains. In doing so, a data set of teachers´ classroom questions has been collected and annotated manually with Bloom´s taxonomy cognitive levels. Preprocessing steps have been applied to convert questions into a suitable representation. Using this data set, the effectiveness of Particle Swarm Classification has been evaluated and compared with four conventional machine learning techniques. The results show that Particle Swarm Classification is promising for tackling classification tasks in Educational Data Mining. Moreover, the results confirm that Particle Swarm Classification with proper confinement mechanism is effective for the classification in high dimensional domains. These conclusions are evidenced by the superior performance of Particle Swarm Classification over the four conventional machine learning techniques.
Keywords :
cognition; data acquisition; data mining; educational administrative data processing; learning (artificial intelligence); particle swarm optimisation; pattern classification; Bloom taxonomy; cognitive levels; confinement mechanism; educational data mining field; high dimensional domains; high dimensional educational data classification; machine learning techniques; particle swarm classification technique; Classification algorithms; Data mining; Databases; Decision trees; Education; Particle swarm optimization; Taxonomy; bloom´s taxonomy; educational data mining; high dimensional data; particle swarm classification; swarm intelligence;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Computer Systems and Applications (AICCSA), 2014 IEEE/ACS 11th International Conference on
Type :
conf
DOI :
10.1109/AICCSA.2014.7073176
Filename :
7073176
Link To Document :
بازگشت