PLDL: A Novel Method for Label Distribution Learning
Journal: The International Arab Journal of Information Technology (Vol.16, No. 6)Publication Date: 2019-11-01
Authors : Venkatanareshbabu Kuppili; Mainak Biswas; Damodar Edla;
Page : 1021-1027
Keywords : Multi-label classification; data mining; label distribution learning; probability density function;
Abstract
The nature, volume and orientation of data have been changed a lot in the last few years. The changed situation has beckoned data scientists to modify traditional algorithms and innovate new methods for processing new type of high volume, extremely complex data. One of the challenges is label ambiguity in the data, where the distribution of the significance of the labels matters. In this paper, a new method named Probabilistic Label Distribution Learning (PLDL) has been proposed for a computing degree of the belongingness. It is based on a proposed new Label Probability Density Function (LPDF) derived from Parzon estimate. The LPDF has been used in Algorithm Adoption K-Nearest Neighbors (AA-KNN) for Label Distribution Learning (LDL). Probability density estimators are used to estimate this ambiguity for each and every label. The overall degree of the belongingness of unseen instance has been evaluated on various real datasets. Comparative performance evaluation in terms of prediction accuracy of the proposed PLDL has been made with Algorithm adaptation KNN, Multilayer Perceptron, Levenberg-Marquardt neural network and layer recurrent neural for Label Distribution Learning. It has been observed that the increase in prediction accuracy for the proposed P
Other Latest Articles
- A New Approach to Improve Association Rules for Big Data in Cloud Environment
- A Proactive Caching Scheme Based on Content Concentration in Content-Centric Networks
- Classifying Sentiment of Dialectal Arabic Reviews: A Semi-Supervised Approach
- Parameter Optimization of Single Sample Virtually Expanded Method
- A Personalized Metasearch Engine Based on MultiAgent System
Last modified: 2019-11-11 21:28:32