Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely:  introduce an ensemble of classifiers-based approach for incremental learning of concept drift, characterized by nonstationary environments (NSEs), where the underlying data distributions change over time. The proposed algorithm, named Learn ++ .NSE, learns from consecutive batches of data without making any assumptions on the nature or rate of drift; it can learn from such environments that experience constant or variable rate of drift, addition or deletion of concept classes, as ll as cyclical drift. The algorithm learns incrementally, as other members of the Learn ++  family of algorithms, that is, without requiring access to previously seen data. Learn ++ .NSE trains one new classifier for each batch of data it receives, and combines these classifiers using a dynamically ighted majority voting. The novelty of the approach is in determining the voting ights, based on each classifier's time-adjusted accuracy on current and past environments. This approach allows the algorithm to recognize, and act accordingly, to the changes in underlying data distributions, as ll as to a possible reoccurrence of an earlier distribution.  evaluate the algorithm on several synthetic datasets designed to simulate a variety of nonstationary environments, as ll as a real-world ather prediction dataset. Comparisons with several other approaches are also included. Results indicate that Learn ++ .NSE can track the changing environments very closely, regardless of the type of concept drift. To allow future use, comparison and benchmarking by interested researchers,  also release our data used in this paper.
['Ryan Elwell', 'Robi Polikar']
I am writing a research paper on a idea Incremental Learning of Concept Drift in Nonstationary Environments
[{"from": "human", "value": "I am writing a research paper on a idea Incremental Learning of Concept Drift in Nonstationary Environments"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely:  introduce an ensemble of classifiers-based approach for incremental learning of concept drift, characterized by nonstationary environments (NSEs), where the underlying data distributions change over time. The proposed algorithm, named Learn ++ .NSE, learns from consecutive batches of data without making any assumptions on the nature or rate of drift; it can learn from such environments that experience constant or variable rate of drift, addition or deletion of concept classes, as ll as cyclical drift. The algorithm learns incrementally, as other members of the Learn ++  family of algorithms, that is, without requiring access to previously seen data. Learn ++ .NSE trains one new classifier for each batch of data it receives, and combines these classifiers using a dynamically ighted majority voting. The novelty of the approach is in determining the voting ights, based on each classifier's time-adjusted accuracy on current and past environments. This approach allows the algorithm to recognize, and act accordingly, to the changes in underlying data distributions, as ll as to a possible reoccurrence of an earlier distribution.  evaluate the algorithm on several synthetic datasets designed to simulate a variety of nonstationary environments, as ll as a real-world ather prediction dataset. Comparisons with several other approaches are also included. Results indicate that Learn ++ .NSE can track the changing environments very closely, regardless of the type of concept drift. To allow future use, comparison and benchmarking by interested researchers,  also release our data used in this paper."}]