Analyzing the performance improvement of hierarchical binary classifiers using ACO through Monte Carlo simulation and multiclass engine vibration data

Hierarchical binary classifiers are often used for multiclass problems when the number of classes is significantly more. The hierarchical structure suffers from a decrease in detection probability when there is an increase in the number of layers and classes. The order and the choice of classifier used in individual blocks of the hierarchical structure affect the overall probability of detection. In this paper, Ant Colony Optimization (ACO) is proposed to optimize the order of the classifier block at each level. It is also proposed to assign a suitable classifier model for the individual classifier block at each level to maximize the overall probability of detection. The proposed techniques also use an Adaptive boosting (Adaboost) classifier model (weighted average of the constructed classifier) as an alternative approach instead of fixing one classifier model at each classifier block and optimizing the classifier block's order using ACO. The description using the probability of false negative (miss-* pM), and the probability of false positive (false alarm-* pF) gives the characteristics of the individual classifier blocks in the hierarchical structure. Monte carlo simulation is performed by randomly assigning values for pM and pF. It was found that the overall detection rate is consistently increasing using the proposed method for various attempts made in the Monte carlo simulation. The performance of the optimized binary hierarchical structures obtained using the proposed technique for the Engine vibration data with 42 classes and 53 block classifiers are compared with the default hierarchical structure. It was observed that there has been a significant improvement in the overall detection rate from 87.235% using the default structure (with Random forest ensemble to classify all classes in the hierarchy) to 90.92% using the proposed technique (ACO with Adaboost). The proposed technique can be used for applications that demand hierarchical, multi-level classifications.

Automated summary

This paper proposes a method that uses Ant Colony Optimization and Adaptive boosting to optimize hierarchical binary classifier structures, in order to improve the overall detection rate. The effectiveness of this method was demonstrated through Monte Carlo simulations and compared with the default structure, showing a significant improvement in detection rate. This method is suitable for applications that require hierarchical multi-level classification.