Application of mutual information-based sequential feature selection to ISBSG mixed data
نویسندگان
چکیده
منابع مشابه
Mutual information based feature selection for mixed data
The problem of feature selection is crucial for many applications and has thus been studied extensively. However, most of the existing methods are designed to handle data consisting only in categorical or in real-valued features while a mix of both kinds of features is often encountered in practice. This paper proposes an approach based on mutual information and the maximal Relevance minimal Re...
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With emergence of new techniques, data in many fields are getting larger and larger, especially in dimensionality aspect. The high dimensionality of data may pose great challenges to traditional learning algorithms. In fact, many of features in large volume of data are redundant and noisy. Their presence not only degrades the performance of learning algorithms, but also confuses end-users in th...
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A feature/input selection method is proposed based on joint mutual information. The new method is better than the existing methods based on mutual information in eliminating redundancy in the inputs. It is applied in a real world application to nd 2-D viewing coordinates for data visualization and to select inputs for a neural network classiier. The result shows that the new method can nd many ...
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Multi-label feature selection is regarded as one of the most promising techniques that can be used to maximize the efficacy and efficiency of multi-label classification. However, because multi-label feature selection algorithms must consider multiple labels concurrently, the task is more difficult than singlelabel feature selection tasks. In this paper, we propose the Mutual Information-based m...
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ژورنال
عنوان ژورنال: Software Quality Journal
سال: 2017
ISSN: 0963-9314,1573-1367
DOI: 10.1007/s11219-017-9391-5