An Efficient Multi-Label SVM Classification Algorithm by Combining Approximate Extreme Points Method and Divide-and-Conquer Strategy

Abstract

Excessive time complexity has severely restricted the application of support vector machine (SVM) in large-scale multi-label classification. Thus, this paper proposes an efficient multi-label SVM classification algorithm by combining approximate extreme points method and divide-and-conquer strategy (AEDC-MLSVM). The AEDC-MLSVM classification algorithm firstly uses the approximate extreme points method to obtain the representative set from the multi-label training data set. While persisting almost all the useful information of multi-label training set, representative set can effectively reduce the scale of multi-label training set. Secondly, to acquire an efficient multi-label SVM classification model, the SVM based on the improved divide-and-conquer strategy is trained on the representative set, which will further improve the training speed and classification performance. The improvement is reflected in two aspects. (1) The improved divide-and-conquer strategy is applied to divide the representative set into subsets and this can ensure that each representative subset contains a certain number of positive and negative instances. This will avoid singular problems and overcome computation load imbalance problem. (2) The different error cost (DEC) method is applied to overcome the label imbalance problem. Effective experiments have proved that the training and testing speed of AEDC-MLSVM classification algorithm can be accelerated substantially while ensuring the classification performance. INDEX TERMS multi-label classification, approximate extreme points method, divide-and-conquer strategy, support vector machine, label imbalance

SVM transformations for Multi-labeled Topics

“…However, the impact of the applied transformation on the SVM model is not highlighted and it could impact the accuracy of the model. [19] used the binary relevance transformation strategy to realize multi-label classification effectively. It applied the divide-andconquer strategy to divide the representative set into subsets and this can ensure that each representative subset contains a certain number of positive and negative instances.…”

SVM transformations for Multi-labeled Topics

“…However, the impact of the applied transformation on the SVM model is not highlighted and it could impact the accuracy of the model. [19] used the binary relevance transformation strategy to realize multi-label classification effectively. It applied the divide-andconquer strategy to divide the representative set into subsets and this can ensure that each representative subset contains a certain number of positive and negative instances.…”