By Huan Liu, Hiroshi Motoda
Because of expanding calls for for dimensionality aid, examine on characteristic choice has deeply and broadly multiplied into many fields, together with computational records, development reputation, computer studying, facts mining, and information discovery. Highlighting present learn concerns, Computational tools of function choice introduces the fundamental strategies and ideas, cutting-edge algorithms, and novel purposes of this instrument.
The publication starts off by way of exploring unsupervised, randomized, and causal characteristic choice. It then studies on a few fresh result of empowering characteristic choice, together with lively function choice, decision-border estimate, using ensembles with self sufficient probes, and incremental characteristic choice. this is often via discussions of weighting and native tools, akin to the ReliefF relations, ok -means clustering, neighborhood characteristic relevance, and a brand new interpretation of aid. The publication hence covers textual content category, a brand new characteristic choice rating, and either constraint-guided and competitive function choice. the ultimate part examines purposes of characteristic choice in bioinformatics, together with function building in addition to redundancy-, ensemble-, and penalty-based characteristic choice.
Through a transparent, concise, and coherent presentation of themes, this quantity systematically covers the foremost thoughts, underlying ideas, and creative functions of function choice, illustrating how this strong software can successfully harness substantial, high-dimensional information and switch it into important, trustworthy details.
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Some of the features may be irrelevant and some of the features may be redundant. Each feature or feature subset needs to be evaluated based on importance by a criterion. Different criteria may select different features. It is actually deciding the evaluation criteria that makes feature selection in clustering difficult. In classification, it is natural to keep the features that are related to the labeled classes. However, in clustering, these class labels are not available. Which features should we keep?
References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19 21 23 25 32 34 35 35 Introduction Many existing databases are unlabeled, because large amounts of data make it difficult for humans to manually label the categories of each instance. Moreover, human labeling is expensive and subjective. Hence, unsupervised learning is needed. , text, images, gene). However, not all of the features domain experts utilize to represent these data are important for the learning task.
6] T. Hastie, R. Tibshirani, and J. Friedman. The Elements of Statistical Learning. Springer, 2001. [7] A. Jakulin and I. Bratko. Testing the significance of attribute interactions. In ICML ’04: Twenty-First International Conference on Machine Learning. ACM Press, 2004. [8] G. John, R. Kohavi, and K. Pfleger. Irrelevant feature and the subset selection problem. In W. Cohen and H. , editors, Machine Learning: Proceedings of the Eleventh International Conference, pages 121–129, New Brunswick, NJ: Rutgers University, 1994.