Multi-Label Classification with PSO based Synthetic Minority Over-Sampling Technique (Psosmote) for Imbalanced Samples
M.Priyadharshini1, L.Pavithira2
1M.Priyadharshini, Assistant Professor, Department of CT, Dr.N.G.P. Arts and Science College, Coimbatore.
2Dr. L. Pavithira, Associate Professor, Department of Computer Applications, CIMAT, Coimbatore.

Manuscript received on November 10, 2019. | Revised Manuscript received on November 17, 2019. | Manuscript published on 30 November, 2019. | PP: 4039-4042 | Volume-8 Issue-4, November 2019. | Retrieval Number: D8437118419/2019©BEIESP | DOI: 10.35940/ijrte.D8437.118419

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Abstract: Recently, the learning from unbalanced data has emerged to be a pre-dominant problem in several applications and in that multi label classification is an evolving data mining task, learning from unbalanced multilabel data is being examined. However, the available algorithms-based SMOTE makes use of the same sampling rate for every instance of the minority class. This leads to sub-optimal performance. To deal with this problem, a new Particle Swarm Optimization based SMOTE (PSOSMOTE) algorithm is proposed. The PSOSMOTE algorithm employs diverse sampling rates for multiple minority class instances and gets the fusion of optimal sampling rates and to deal with classification of unbalanced datasets. Then, Bayesian technique is combined with Random forest for multi-label classification (BARF-MLC) is to address the inherent label dependencies among samples such as ML-FOREST classifier, Predictive Clustering Trees (PCT), Hierarchy of Multi Label Classifier (HOMER) by taking the different metrics including precision, recall, F-measure, Accuracy and Error Rate.
Keywords: Multi-Label Classification, Multi-Class Imbalance, PSO, SMOTE, Bayesian Approach. (Drngpasc 2019-20 CS016).
Scope of the Article: Classification.