- Real-world problems can be high-dimensional, complex, and noisy - More data does not imply more information - Different approaches deal with the so-called curse of dimensionality to reduce irrelevant information - A process with multidimensional information is not necessarily easy to interpret nor process - In some real-world applications, the number of elements of a class is clearly lower than the other. The models tend to assume that the importance of the analysis belongs to the majority class and this is not usually ...
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- Real-world problems can be high-dimensional, complex, and noisy - More data does not imply more information - Different approaches deal with the so-called curse of dimensionality to reduce irrelevant information - A process with multidimensional information is not necessarily easy to interpret nor process - In some real-world applications, the number of elements of a class is clearly lower than the other. The models tend to assume that the importance of the analysis belongs to the majority class and this is not usually the truth - The analysis of complex diseases such as cancer are focused on more-than-one dimensional omic data - The increasing amount of data thanks to the reduction of cost of the high-throughput experiments opens up a new era for integrative data-driven approaches - Entropy-based approaches are of interest to reduce the dimensionality of high-dimensional data
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