In this work, we propose a hybrid binary classifier which combines a decision tree with a support vector machine. The proposed hybrid model has the advantages of improved accuracy and easy interpretability. The model will be useful for feature selection cum classification tasks in real-world supervised learning problems. Numerical evidence is also provided using 25 standard data sets from various fields to assess the performance of the model. Performance of the proposed hybrid binary classifier is quite better when compared to individual classifiers.

The hybrid binary classifier, combining decision tree and support vector machine, improves accuracy and interpretability in pattern classification tasks compared to individual classifiers.

A Hybrid Binary Classifier for Pattern Classification

Key Takeaway: The hybrid binary classifier, combining decision tree and support vector machine, improves accuracy and interpretability in pattern classification tasks compared to individual classifiers.