Results 221 to 230 of about 774,252 (269)
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Pattern Recognition, 2002
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Petr Somol, Pavel Pudil
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zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Petr Somol, Pavel Pudil
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Information Sciences, 2019
Abstract In state-of-the-art big-data applications, the process of building machine learning models can be very challenging due to continuous changes in data structures and the need for human interaction to tune the variables and models over time. Hence, expedited learning in rapidly changing environments is required. In this work, we address this
Michal Moran, Goren Gordon
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Abstract In state-of-the-art big-data applications, the process of building machine learning models can be very challenging due to continuous changes in data structures and the need for human interaction to tune the variables and models over time. Hence, expedited learning in rapidly changing environments is required. In this work, we address this
Michal Moran, Goren Gordon
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Pattern Recognition, 1999
In fuzzy classi"er systems the classi"cation is obtained by a number of fuzzy If}Then rules including linguistic terms such as Low and High that fuzzify each feature. This paper presents a method by which a reduced linguistic (fuzzy) set of a labeled multi-dimensional data set can be identi"ed automatically.
M. Ramze Rezaee +3 more
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In fuzzy classi"er systems the classi"cation is obtained by a number of fuzzy If}Then rules including linguistic terms such as Low and High that fuzzify each feature. This paper presents a method by which a reduced linguistic (fuzzy) set of a labeled multi-dimensional data set can be identi"ed automatically.
M. Ramze Rezaee +3 more
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Feature Selection and Feature Extraction: Highlights
Proceedings of the 2021 5th International Conference on Intelligent Systems, Metaheuristics & Swarm Intelligence, 2021In recent years, big data deluges have resulted in exciting data science opportunities. In particular, there is always a desire to extract the most from different data sources. To address it, a promising and recurring task is to perform feature selection and feature extraction.
Hiu-Man Wong +7 more
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2009 IEEE International Conference on Data Mining Workshops, 2009
Traditional feature selection algorithms require a large number of labeled training instances to find out the most informative subset of features. However, in many real-world applications, the labeled data are often difficult, expensive or time-consuming to obtain.
Wei Bi, Yuan Shi, Zhen-zhong Lan
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Traditional feature selection algorithms require a large number of labeled training instances to find out the most informative subset of features. However, in many real-world applications, the labeled data are often difficult, expensive or time-consuming to obtain.
Wei Bi, Yuan Shi, Zhen-zhong Lan
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Feature Interaction for Streaming Feature Selection
IEEE Transactions on Neural Networks and Learning Systems, 2021Traditional 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 +3 more
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2015 IEEE International Conference on Computer Vision (ICCV), 2015
Feature dimensionality reduction has been widely used in various computer vision tasks. We explore feature selection as the dimensionality reduction technique and propose to use a structured approach, based on the Markov Blanket (MB), to select features. We first introduce a new MB discovery algorithm, Simultaneous Markov Blanket (STMB) discovery, that
Tian Gao, Ziheng Wang 0001, Qiang Ji
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Feature dimensionality reduction has been widely used in various computer vision tasks. We explore feature selection as the dimensionality reduction technique and propose to use a structured approach, based on the Markov Blanket (MB), to select features. We first introduce a new MB discovery algorithm, Simultaneous Markov Blanket (STMB) discovery, that
Tian Gao, Ziheng Wang 0001, Qiang Ji
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Applied Intelligence, 1998
Feature selection is a problem of finding relevant features. When the number of features of a dataset is large and its number of patterns is huge, an effective method of feature selection can help in dimensionality reduction. An incremental probabilistic algorithm is designed and implemented as an alternative to the exhaustive and heuristic approaches.
Liu, H., Setiono, R.
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Feature selection is a problem of finding relevant features. When the number of features of a dataset is large and its number of patterns is huge, an effective method of feature selection can help in dimensionality reduction. An incremental probabilistic algorithm is designed and implemented as an alternative to the exhaustive and heuristic approaches.
Liu, H., Setiono, R.
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Feature Selection Boosted by Unselected Features
IEEE Transactions on Neural Networks and Learning Systems, 2022Feature selection aims to select strongly relevant features and discard the rest. Recently, embedded feature selection methods, which incorporate feature weights learning into the training process of a classifier, have attracted much attention. However, traditional embedded methods merely focus on the combinatorial optimality of all selected features ...
Wei Zheng +5 more
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Proceedings of the 30th annual international ACM SIGIR conference on Research and development in information retrieval, 2007
Ranking is a very important topic in information retrieval. While algorithms for learning ranking models have been intensively studied, this is not the case for feature selection, despite of its importance. The reality is that many feature selection methods used in classification are directly applied to ranking.
Xiubo Geng +3 more
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Ranking is a very important topic in information retrieval. While algorithms for learning ranking models have been intensively studied, this is not the case for feature selection, despite of its importance. The reality is that many feature selection methods used in classification are directly applied to ranking.
Xiubo Geng +3 more
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