Classifying objects into multiple categories is a fundamental task in machine learning, known as multiclass classification. However, this task becomes challenging when dealing with numerous classes, leading to the need for specialized techniques. In this article, we survey various approaches to tackle multiclass classification issues, focusing on hybrid models that combine different techniques to achieve better performance.
Hybrid Models
Hybrid models are composite techniques that merge multiple algorithms to overcome the limitations of individual methods. These models can be further divided into two categories: Reducing Multiclass to Binary and One-vs-One (1-vs-1) Multiclass Methodologies.
Reducing Multiclass to Binary Decision
This approach aims to transform multiple classification problems into binary classification ones by selecting the most relevant features and classes. The process involves feature selection, where the irrelevant features are eliminated, followed by converting the multiclass problem into a binary one using the selected features.
One-vs-One (1-vs-1) Multiclass Methodology
In this method, each class is compared to every other class individually, and the model predicts the winner for each pair of classes. The performance of these pairwise comparisons is then combined to determine the overall classification result.
Performances of Two Approaches
The article evaluates the performances of both approaches using various datasets and algorithms. The results show that the one-vs-one (1-vs-1) multiclass methodology outperforms reducing multiclass to binary decision in most cases, especially when dealing with high-dimensional data sets. However, reducing multiclass to binary decision is computationally more efficient than the 1-vs-1 approach.
Conclusion
In conclusion, this article provides a comprehensive overview of hybrid models for multiclass classification, highlighting their advantages and limitations. By combining different techniques, these models can improve the accuracy and efficiency of multiclass classification tasks. The choice between the two approaches depends on the specific problem and dataset used, with the one-vs-one (1-vs-1) methodology generally outperforming reducing multiclass to binary decision. Understanding the strengths and weaknesses of these techniques can help researchers and practitioners select the most appropriate approach for their classification problems.
