Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: In many application areas, it is important to detect outliers. Traditional engineering approach to outlier detection is that  start with some normal values x 1 ,...,x n , compute the sample average E, the sample standard variation a, and then mark a value x as an outlier if x is outside the k 0 -sigma interval [E - k 0  . σ, E + k 0  . σ] (for some preselected parameter k 0 ). In real life,  often have only interval ranges [x i ,x i ] for the normal values x 1 ,...,x n . In this case,  only have intervals of possible values for the bounds E - k 0  . σ and E + k 0  . a.  can therefore identify outliers as values that are outside all k 0 -sigma intervals. ,  analyze the computational complexity of these outlier detection problems, and provide efficient algorithms that solve some of these problems (under reasonable conditions).
['Vladik Kreinovich', 'Luc Longpre', 'Praveen Patangay', 'Scott Ferson', 'Lev R. Ginzburg']
I am writing a research paper on a idea Outlier detection under interval uncertainty: Algorithmic solvability and computational complexity
[{"from": "human", "value": "I am writing a research paper on a idea Outlier detection under interval uncertainty: Algorithmic solvability and computational complexity"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: In many application areas, it is important to detect outliers. Traditional engineering approach to outlier detection is that  start with some normal values x 1 ,...,x n , compute the sample average E, the sample standard variation a, and then mark a value x as an outlier if x is outside the k 0 -sigma interval [E - k 0  . \u03c3, E + k 0  . \u03c3] (for some preselected parameter k 0 ). In real life,  often have only interval ranges [x i ,x i ] for the normal values x 1 ,...,x n . In this case,  only have intervals of possible values for the bounds E - k 0  . \u03c3 and E + k 0  . a.  can therefore identify outliers as values that are outside all k 0 -sigma intervals. ,  analyze the computational complexity of these outlier detection problems, and provide efficient algorithms that solve some of these problems (under reasonable conditions)."}]