Support Vector Machine classifiers are widely used in many classification tasks. However, they have two considerable weaknesses, Unclassifiable Region (UR) in multiclass classification and outliers. In this research, we address these problems by introducing Probabilistic Least Square Twin Support Vector Machine (PLS-TSVM). The proposed algorithm introduces continuous and probabilistic outputs over the model obtained by Least-Square Twin Support Vector Machine (LS-TSVM) method with both linear and polynomial kernel functions. PLS-TSVM not only solves the unclassifiable region problem by introducing a continuous output value membership function, but it also reduces the adverse effects of noisy data and outliers. For showing the superiority of our proposed method, we have conducted experiments on various UCI datasets. In the most cases, higher or competitive accuracy to other methods have been obtained such that in some UCI datasets, PLS-TSVM could obtain up to 99.90% of classification accuracy. Moreover, PLS-TSVM has been evaluated against ”one-against-all” and ”one-against-one” approaches on several well-known video datasets such as Weizmann, KTH, and UCF101 for human action recognition task. The results show the higher accuracy of PLS-TSVM compared to its counterparts. Specifically, the proposed algorithm could improve respectively about 12.2%, 2.8%, and 12.1% of classification accuracy in three video datasets compared to the standard SVM and LS-TSVM classifiers. The final results indicate that the proposed algorithm could achieve better overall performances than the literature.

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