Results 241 to 250 of about 4,817,340 (289)

Coarse-to-Fine Image Aesthetics Assessment With Dynamic Attribute Selection

IEEE transactions on multimedia
Image aesthetics assessment (IAA) is an interesting but challenging task, owing to the ineffable nature of human sense of beauty. The study of IAA has evolved from simple binary classification to more complex score regression and distribution prediction.
Yipo Huang, Leida Li, Pengfei Chen, Jinjian Wu, Yuzhe Yang, Yaqian Li, Guangming Shi   +6 more
semanticscholar   +1 more source

Attribute selection for modelling

Future Generation Computer Systems, 1997
Abstract Modelling a target attribute by other attributes in the data is perhaps the most traditional data mining task. When there are many attributes in the data, one needs to know which of the attribute(s) are relevant for modelling the target, either as a group or the one feature that is most appropriate to select within the model construction ...
Igor Kononenko, Se June Hong
openaire   +1 more source

Third-party reverse logistics provider selection: A computational semantic analysis-based multi-perspective multi-attribute decision-making approach

Expert systems with applications, 2021
In the era of circular economies, governments and consumers are increasingly aware of environmental protection, which encourages enterprises to devote more attention to reverse logistics (RL).
Zhen-Song Chen, Xuan Zhang, K. Govindan, Xian-Jia Wang, K. Chin   +4 more
semanticscholar   +1 more source

Attributional Style, Task Selection and Achievement

Journal of Educational Psychology, 1979
The role of causal attributions in determining motivation to achieve has been the object of intensive study with generally interesting and valuable results (Dweck & Goetz, 1978; Weiner, in press). Thus, it seems quite clear that causal attributions play a critical role in determining the perception of success and failure as such (cf.
Leslie J. Fyans, Martin L. Maehr
openaire   +1 more source

Greedy Attribute Selection

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
openaire   +1 more source

Lazy attribute selection: Choosing attributes at classification time

Intelligent Data Analysis, 2011
Attribute 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 ...
Pereira, Rafael B., Plastino, Alexandre, Zadrozny, Bianca, de C. Merschmann, Luiz Henrique, Freitas, Alex A.   +4 more
openaire   +1 more source

Filter-based Attribute Selection Approach for Intrusion Detection using k-Means Clustering and Sequential Minimal Optimization Techniq

2019 Amity International Conference on Artificial Intelligence (AICAI), 2019
Intrusion Detection Systems (IDS) are administered by analysts for analysing system logs or data packets to predict malware in the network traffic. IDS automate this process for continuously increasing data in the network by using techniques based on ...
A. Chandra, S. Khatri, Rajbala Simon
semanticscholar   +1 more source

A data sampling and attribute selection strategy for improving decision tree construction

Expert systems with applications, 2019
Decision trees are efficient means for building classification models due to the compressibility, simplicity and ease of interpretation of their results. However, during the construction phase of decision trees, the outputs are often large trees that are
Nour El Islem Karabadji, Ilyes Khelf, H. Seridi-Bouchelaghem, Sabeur Aridhi, D. Rémond, W. Dhifli   +5 more
semanticscholar   +1 more source

Super Attribute Representative for Decision Attribute Selection

2011
Soft 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, Tutut Herawan, Mustafa Mat Deris   +2 more
openaire   +1 more source

Attribute Selection for Partially Labeled Categorical Data By Rough Set Approach

IEEE Transactions on Cybernetics, 2017
Attribute selection is considered as the most characteristic result in rough set theory to distinguish itself to other theories. However, existing attribute selection approaches can not handle partially labeled data.
Jianhua Dai, Q. Hu, Jinghong Zhang, Hu Hu, Nenggan Zheng   +4 more
semanticscholar   +1 more source