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In this work, a new feature selection algorithm has been proposed by using an attribute selection and tuple selection.
Therefore, an attribute selection task pursues the following goals: (i) to reach better classification results, (ii) to generate more efficient models, and (iii) to reduce the data dimensionality and therefore computational costs.
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It contains several improvements, including: choosing an appropriate attribute selection measure, handling training data with missing attribute values, handling attributes with differing costs, and handling continuous attributes [ 8].
Several improvements are included in J48 algorithm, such as choosing an appropriate attribute selection measure, and handling training data with missing attribute values, attributes with differing costs, and continuous attributes.
Best attributes for the models were selected based on a supervised attribute selection method called wrapper subset evaluator (WSE).
We evaluated the algorithms with a filter of attribute selection for two reasons.
Furthermore, Weka algorithms were evaluated twice for each one of those settings: (A) with no attribute selection filter on the training sets and (B) with attribute selection filter.
In the present work, a combination of attribute selection and data sampling is used to overcome these problems.
A genetic algorithm attribute selection method was used to identify clinical predictors for patients' rechallenge status.
Moore and White (2007a) specifically evaluated whether including preprocessed attribute quality estimates using TuRF (see above) in a multiobjective fitness function improved attribute selection over a random search that just uses accuracy as the fitness of the models.
Separate models were developed on fingerprints selected using attribute selection modules rm-useless and CfsSubsetEval of Weka.
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