Text Classification using Incremental Learning for many classes of Support Vector Machines

Abstract

This study introduces class-incremental learning (CIL). This framework improves multi-class data support vector machines (SVMs). CIL is incremental feature selection and training. Both steps update SVM classifiers as new classes are added to a system. CIL learns one binary sub-classifier and reuses classifier models for subsequent classes. Another feature selection stage follows. Testing applies the classifier to subspacerelevant vector projections. The CIL system is compatible with any binary classification approaches that are used for text analysis. According to the findings of our research, the CIL-based SVM beat the most popular batch SVM learning methods, such as divide-by-2, 1-against-1, and 1-against-rest, and trained faster. These learning methods are used to train SVMs.

Emotion detection using Word2Vec and convolution neural networks

“…The proposed approach achieved the state-of-the-art results with comparison of other machine learning algorithms. We also experimented with other machine learning algorithms such as RF [18], Naïve Bayes [19], LR [20], and SVM [21] along with TF-IDF [22] word embedding technique. We found that our proposed approach is performing well in all seven different categories.…”