Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: A challenging problem in hierarchical classification is to leverage the hierarchical relations among classes for improving classification performance. An even greater challenge is to do so in a manner that is computationally feasible for large scale problems.  proposes a set of Bayesian methods to model hierarchical dependencies among class labels using multivariate logistic regression. Specifically, the parent-child relationships are modeled by placing a hierarchical prior over the children nodes centered around the parameters of their parents; thereby encouraging classes nearby in the hierarchy to share similar model parameters.  present variational algorithms for tractable posterior inference in these models, and provide a parallel implementation that can comfortably handle large-scale problems with hundreds of thousands of dimensions and tens of thousands of classes.  run a comparative evaluation on multiple large-scale benchmark datasets that highlights the scalability of our approach and shows improved performance over the other state-of-the-art hierarchical methods.
['Siddharth Gopal', 'Yiming Yang', 'Bing Bai', 'Alexandru Niculescu-Mizil']
I am writing a research paper on a idea Bayesian models for Large-scale Hierarchical Classification
[{"from": "human", "value": "I am writing a research paper on a idea Bayesian models for Large-scale Hierarchical Classification"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: A challenging problem in hierarchical classification is to leverage the hierarchical relations among classes for improving classification performance. An even greater challenge is to do so in a manner that is computationally feasible for large scale problems.  proposes a set of Bayesian methods to model hierarchical dependencies among class labels using multivariate logistic regression. Specifically, the parent-child relationships are modeled by placing a hierarchical prior over the children nodes centered around the parameters of their parents; thereby encouraging classes nearby in the hierarchy to share similar model parameters.  present variational algorithms for tractable posterior inference in these models, and provide a parallel implementation that can comfortably handle large-scale problems with hundreds of thousands of dimensions and tens of thousands of classes.  run a comparative evaluation on multiple large-scale benchmark datasets that highlights the scalability of our approach and shows improved performance over the other state-of-the-art hierarchical methods."}]