PLDL : a novel method for label distribution learning

Abstract EN

he 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 PLDL is highly statistically significant for most of the real datasets when compared with the standard algorithms for LDL.