A Survey & Current Research Challenges in Meta Learning Approaches based on Dataset Characteristics
Nikita Bhatt1, Amit Thakkar2, Amit Ganatra3
1Nikita Bhatt, Department of Computer Engineering, Charotar Institute of Technology (Faculty of Technology and Engineering), Charotar UniversityofTechnologyChanga,Anand, Gujarat, India.
2Amit Thakkar, Department of Information Technology, Charotar Institute of Technology (Faculty of Technology and Engineering), Charotar UniversityofTechnologyChanga,Anand, Gujarat, India.
3Amit Ganatra, Department of Computer Engineering, Charotar Institute of Technology (Faculty of Technology and Engineering), Charotar UniversityofTechnologyChanga,Anand, Gujarat, India.
Manuscript received on February 15, 2012. | Revised Manuscript received on February 20, 2012. | Manuscript published on March 05, 2012. | PP: 239-247 | Volume-2 Issue-1, March 2012. | Retrieval Number: A0426022112 /2012©BEIESP
Open Access | Ethics and Policies | Cite
© The Authors. Published By: Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Classification is a process that predicts class of objects whose class label is unknown. According to No Free Lunch (NFL) theorem, there is no single classifier that performs better on all datasets. Meta learning is one of the approaches that acquired knowledge based on the past experience. The knowledge in Meta-Learning is acquired from a set of meta-examples which stores the features of the problem and the performance obtained by executing a set of candidate algorithms on Meta Features. Based on the experience acquired by the system during training phase, ranking of the classifiers is provided based on considering various measures of classifiers.
Keywords: Classification, Meta Learning, Ranking