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Committee Selection using Attribute Approvals [PDF]
International Joint Conference on Autonomous Agents and Multiagent Systems, 2021 We consider the problem of committee selection from a fixed set of candidates where each candidate has multiple quantifiable attributes. Instead of voting for a candidate, a voter is allowed to approve the preferred attributes of a given candidate. Though attribute-based preferences capture several important real-life scenarios, committee selection ...
Venkateswara Rao Kagita +4 more
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1994
Abstract Many real-world domains bless us with a wealth of attributes to use for learning. This blessing is often a curse: most inductive methods generalize worse given too many attributes than if given a good subset of those attributes. We examine this problem for two learning tasks taken from a calendar scheduling domain.
Rich Caruana, Dayne Freitag
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Abstract Many real-world domains bless us with a wealth of attributes to use for learning. This blessing is often a curse: most inductive methods generalize worse given too many attributes than if given a good subset of those attributes. We examine this problem for two learning tasks taken from a calendar scheduling domain.
Rich Caruana, Dayne Freitag
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Correlates of Selected Physical Attributes
Research Quarterly. American Association for Health, Physical Education and Recreation, 1969(1969). Correlates of Selected Physical Attributes. Research Quarterly. American Association for Health, Physical Education and Recreation: Vol. 40, No. 3, pp. 637-639.
L J, Dowell, C W, Landiss, E, Mamaliga
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Attribute and object selection queries on objects with probabilistic attributes
ACM Transactions on Database Systems, 2012Modern data processing techniques such as entity resolution, data cleaning, information extraction, and automated tagging often produce results consisting of objects whose attributes may contain uncertainty. This uncertainty is frequently captured in the form of a set of multiple mutually exclusive value choices for each uncertain ...
Rabia Nuray-Turan +3 more
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Lazy attribute selection: Choosing attributes at classification time
Intelligent Data Analysis, 2011Attribute selection is a data preprocessing step which aims at identifying relevant attributes for the target machine learning task – namely classification in this paper. In this paper, we propose a new attribute selection strategy – based on a lazy learning approach – which postpones the identification of relevant attributes until an instance is ...
Rafael B. Pereira +4 more
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Super Attribute Representative for Decision Attribute Selection
2011Soft set theory proposed by Molodstov is a general mathematic tool for dealing with uncertainties. Recently, several algorithms had been proposed for decision making using soft set theory. However, these algorithms still concern on a Boolean-valued information system. In this paper, Support Attribute Representative (SAR), a soft set based technique for
Rabiei Mamat +2 more
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On sequential selection of attributes to be discretized for authorship attribution
2017 IEEE International Conference on INnovations in Intelligent SysTems and Applications (INISTA), 2017Different data mining techniques are employed in stylometry domain for performing authorship attribution tasks. Sometimes to improve the decision system the discretization of input data can be applied. In many cases such approach allows to obtain better classification results.
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Nonlinear feature selection on attributed networks
Neurocomputing, 2020Abstract The acceleratinnsional nodal attributes in various data mining tasks highlights the significance of feature selection on the networked data. Due to the lack of class labels of nodes, many feature selection methods are proposed in semi-supervised or unsupervised manners in various scenarios instead of supervised ones.
Zhongping Lin +4 more
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Adaptive Feature Selection With Augmented Attributes
IEEE Transactions on Pattern Analysis and Machine Intelligence, 2023In many dynamic environment applications, with the evolution of data collection ways, the data attributes are incremental and the samples are stored with accumulated feature spaces gradually. For instance, in the neuroimaging-based diagnosis of neuropsychiatric disorders, with emerging of diverse testing ways, we get more brain image features over time.
Chenping Hou +3 more
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Stochastic Attribute Selection Committees
1998Classifier committee learning methods generate multiple classifiers to form a committee by repeated application of a single base learning algorithm. The committee members vote to decide the final classification. Two such methods, Bagging and Boosting, have shown great success with decision tree learning.
Zijian Zheng 0002, Geoffrey I. Webb
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