Results 251 to 260 of about 288,301 (297)

The Impact of Features on Feature Location

2019 International Conference on Frontiers of Information Technology (FIT), 2019
Presence of large number of Feature Location Techniques (FLTs) poses difficulties when selecting an appropriate FLT, given a software maintenance task. This problem is aggravated by extensive heterogeneity in empirical designs employed to evaluate the FLTs and one such heterogeneity that may feed into conflicting findings across studies, is the feature
Abdul Qayum, Abdul Razzaq
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Reconstruction of feature volumes and feature suppression

Proceedings of the seventh ACM symposium on Solid modeling and applications, 2002
This paper describes a systematic algorithm for reconstructing the feature volume from a set of faces in a solid model. This algorithm serves a dual purpose. Firstly, the algorithm generates the feature volume by extending or contracting the neighboring faces of the set of faces. Secondly, the algorithm may also be used to remove (or suppress) the face-
Sashikumar Venkataraman, Milind A. Sohoni   +1 more
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Why Feature Featured Research?

Journal of the American Society of Echocardiography, 2008
ersity 8195. This issue of the Journal of the American Society of Echocardiography (JASE) features a “featured research” article by Drs. Marielle SchererCrosbie and Helene Thibault. A little background may be helpful for some readers who may ask themselves the following questions: Why is JASE featuring featured research?
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Feature modification in incremental feature generation

Computer-Aided Design, 1995
Abstract Feature interaction is a common problem in feature generation methods such as incremental feature generation, automatic feature extraction, feature-based design and manual feature definition. Research on feature interaction involves analysing the interaction relationship, decomposing the interacted features into atomic or single features ...
Hyowon Sun, Rashpal S. Ahluwalia
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Feature Interaction for Streaming Feature Selection

IEEE Transactions on Neural Networks and Learning Systems, 2021
Traditional feature selection methods assume that all data instances and features are known before learning. However, it is not the case in many real-world applications that we are more likely faced with data streams or feature streams or both. Feature streams are defined as features that flow in one by one over time, whereas the number of training ...
Peng Zhou 0008, Peipei Li 0001, Shu Zhao 0005, Xindong Wu 0001   +3 more
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Importance Degree of Features and Feature Selection

2009 Sixth International Conference on Fuzzy Systems and Knowledge Discovery, 2009
A novel measure, importance degree of features, is proposed to rank the features. And a new filter method is presented to carry out feature selection based on such measure. The monotonic property of this proposed measure can reduce the search space, which results in enhancing learning efficiency.
Di Xiao, Junfeng Zhang
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Utilizing feature location techniques for feature addition and feature enhancement

Proceedings of the 29th ACM/IEEE International Conference on Automated Software Engineering, 2014
Additions and enhancements to software are prevalent issues within software development. When developers want to make enhancements to a piece of software they first have to build up an understanding of the relevant parts of the system. There are a number of challenges within this: where is the best place to add changes, what to reuse and identifying ...
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Feature-Modelling — Design by Feature

1999
Eine durchgangige Rechnerunterstutzung und damit auch ganzheitliche Integration der im Produktentwicklungsprozes anfallenden Aufgaben sowie der eingesetzten Anwendungssysteme ist in zunehmenden Mase notwendig um den Erfordernissen des Marktes gerecht zu werden.
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Group Feature Selection with Streaming Features

2013 IEEE 13th International Conference on Data Mining, 2013
Group feature selection makes use of structural information among features to discover a meaningful subset of features. Existing group feature selection algorithms only deal with pre-given candidate feature sets and they are incapable of handling streaming features.
Hai-Guang Li, Xindong Wu 0001, Zhao Li 0007, Wei Ding 0003   +3 more
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Feature condensing algorithm for feature selection

2008 19th International Conference on Pattern Recognition, 2008
A new unsupervised filter-based feature selection method is introduced. Its principle consists in merging similar features into clusters using a distance measure derived from the correlation coefficient. Subsequently, only one representative feature is selected from each cluster.
Pavel Krízek, Josef Kittler, Václav Hlavác   +2 more
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