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    Eric Bloedorn and Ryszard S. Michalski

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    This paper presents a method for data-driven constructive induction, which generates new problemoriented attributes by combining the original attributes according to a variety of heuristic rules. The combination of attributes are defined by different logical and/or mathematical operators, thus producing a potentially very large space of features. This space is reduced by applying an "attribute quality" evaluation function which selects the "best" set of features. The data, enhanced with the new attributes, is used to generate rules which are then evaluated by a "rule quality" function. Attribute construction and rule generation is repeated until a termination condition is satisfied. Attributes produced by the method often represent meaningful and useful concepts. The program, AQ17-DCI, implementing the method has been experimentally applied to a number of problems and produces very satisfactory results. These results are comparable to the best existing machine learning methods. key wo...

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    Title : Eric Bloedorn and Ryszard S. Michalski
    Abstract : This paper presents a method for data-driven constructive induction, which generates new problemoriented attributes by combining the original attributes according to a variety of heuristic rules. The combination of attributes are defined by different logical and/or mathematical operators, thus producing a potentially very large space of features. This space is reduced by applying an "attribute quality" evaluation function which selects the "best" set of features. The data, enhanced with the new attributes, is used to generate rules which are then evaluated by a "rule quality" function. Attribute construction and rule generation is repeated until a termination condition is satisfied. Attributes produced by the method often represent meaningful and useful concepts. The program, AQ17-DCI, implementing the method has been experimentally applied to a number of problems and produces very satisfactory results. These results are comparable to the best existing machine learning methods. key wo...
    Subject : unspecified
    Area : Computer Science
    Language : English
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    Url : http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.52.5864&rep=rep1&type=pdf
    Doi : 10.1.1.52.5864

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