Condition estimation for regression and feature selection
نویسندگان
چکیده
منابع مشابه
Feature Selection for Regression Problems
Feature subset selection is the process of identifying and removing from a training data set as much irrelevant and redundant features as possible. This reduces the dimensionality of the data and may enable regression algorithms to operate faster and more effectively. In some cases, correlation coefficient can be improved; in others, the result is a more compact, easily interpreted representati...
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Abstract. This paper presents a new algorithm to perform regression estimation, in both the inductive and transductive setting. The estimator is defined as a linear combination of functions in a given dictionary. Coefficients of the combinations are computed sequentially using projection on some simple sets. These sets are defined as confidence regions provided by a deviation (PAC) inequality o...
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from Wikipedia A feature selection algorithm can be seen as the combination of a search technique for proposing new feature subsets, along with an evaluation measure which scores the different feature subsets. The simplest algorithm is to test each possible subset of features finding the one which minimizes the error rate. This is an exhaustive search of the space, and is computationally intrac...
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For classification problems, the risk is often the criterion to be eventually minimised. It can thus naturally be used to assess the quality of feature subsets in feature selection. However, in practice, the probability of error is often unknown and must be estimated. Also, mutual information is often used as a criterion to assess the quality of feature subsets, since it can be seen as an imper...
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ژورنال
عنوان ژورنال: Journal of Computational and Applied Mathematics
سال: 2020
ISSN: 0377-0427
DOI: 10.1016/j.cam.2019.03.041