Programs
- Ph. D. in Mechanical Engineering -
- B.Tech. in Artificial Intelligence (AI) and Data Science (Medical Engineering) - Undergraduate
Publication Type : Conference Paper
Publisher : Second International Symposium on Emerging Topics in Computing and Communications, International Conference on Advances in Computing, Communications and Informatics (ICACCI)
Source : Second International Symposium on Emerging Topics in Computing and Communications, International Conference on Advances in Computing, Communications and Informatics (ICACCI), IEEE, Jaipur, India (2016)
Url : https://ieeexplore.ieee.org/abstract/document/7732470
Keywords : antecedent labels, cancer prognosis prediction, class label prediction, Clustering algorithms, consequent labels, data instance, data normalization, Euclidean distance metric, Feature space, FP growth, FP-growth algorithm, Image annotation, Informatics, K-means, logistic regression, Logistics, measurement, MLC-LR, multi-label data set, multilabel classification problem, Pattern classification, pattern clustering, personality prediction, Prediction algorithms, problem transformation method, Regression analysis, Testing, Training
Campus : Amritapuri
School : Department of Computer Science and Engineering, School of Engineering
Department : Computer Science
Verified : No
Year : 2016
Abstract : Numerous class labels associated with each data instance is the main feature of any multi-label classification (MLC) problem. Correct prediction of class labels related to any test data is a big challenge in this domain. MLC can be applied in many fields such as personality prediction, cancer prognosis prediction, image annotation etc. In this paper(MLC-LR), we have employed problem transformation method for solving MLC. The proposed method uses initially clustering in the feature space. It is then followed by FP-growth algorithm for finding the relationship between labels. Once the desired clusters are obtained, then normalization of data associated with each cluster is performed. Also logistic regression is then applied over the normalized data for each particular cluster pertaining to all labels. When a new instance arrives in the testing phase, immediately the nearby cluster is identified by means of Euclidean distance metric as the measure. Rules related to label space for the nearby cluster is extracted to check for hypothesis of each antecedent label. If the calculated value is higher than a predefined threshold, it is assumed that both antecedent and consequent labels as the estimated labels for that test instance.
Cite this Research Publication : Prathibhamol CP, Jyothy, K. V., and Noora, B., “Multi label classification based on logistic regression (MLC-LR)”, in Second International Symposium on Emerging Topics in Computing and Communications, International Conference on Advances in Computing, Communications and Informatics (ICACCI), Jaipur, India, 2016