Results 231 to 240 of about 693,025 (273)

A method for Handling Inconsistencies in Rule-based Classifiers

IEEE Latin America Transactions, 2008
Researches on distributed data mining have as the main interest the development of algorithms and approaches that make possible the analysis of large and physically distributed datasets proposing better solutions in terms of costs and computational complexity.
Fabricio Enembreck, Scalabrin E E
exaly   +2 more sources

Occlusion Handling via Random Subspace Classifiers for Human Detection

IEEE Transactions on Cybernetics, 2014
This paper describes a general method to address partial occlusions for human detection in still images. The random subspace method (RSM) is chosen for building a classifier ensemble robust against partial occlusions. The component classifiers are chosen on the basis of their individual and combined performance.
Ludmila Kuncheva, David Vazquez Bermudez, Antonio Lopez   +2 more
exaly   +4 more sources

An Analysis on Performance of Different Type Classifiers in Handling Big Data Sets

Frontiers in Artificial Intelligence and Applications, 2019
Ali Selamat
exaly   +2 more sources

A Classifier Capable of Handling New Attributes

2007 IEEE Symposium on Computational Intelligence and Data Mining, 2007
During knowledge acquisition, a new attribute can be added at any time. In such a case, rule generated by the training data with the former attribute set can not be used. Moreover, the rule can not be combined with the new data set with the newly added attribute(s) using the existing algorithms.
Dong-Hun Seo, Chi-Hwa Song, Won Don Lee
openaire   +2 more sources

HANDLING AMBIGUOUS VALUES IN INSTANCE-BASED CLASSIFIERS

International Journal on Artificial Intelligence Tools, 2008
In an attempt to automate evaluation of network intrusion detection systems, we encountered the problem of ambiguously described learning examples. For instance, an attribute's value, or a class label, in a given example was known to be a or b but definitely not c or d.
Hans Holland, Miroslav Kubat, Jan Zizka
openaire   +2 more sources

Using Classifier diversity to handle label noise

2015 International Joint Conference on Neural Networks (IJCNN), 2015
It is widely known in the machine learning community that class noise can be (and often is) detrimental to inducing a model of the data. Many current approaches use a single, often biased, measurement to determine if an instance is noisy. A biased measure may work well on certain data sets, but it can also be less effective on a broader set of data ...
Michael R. Smith 0002, Tony R. Martinez
openaire   +1 more source

Binary naive possibilistic classifiers: Handling uncertain inputs

International Journal of Intelligent Systems, 2009
Summary: Possibilistic networks are graphical models particularly suitable for representing and reasoning with uncertain and incomplete information. According to the underlying interpretation of possibilistic scales, possibilistic networks are either quantitative (using product-based conditioning) or qualitative (using min-based conditioning).
Salem Benferhat, Karim Tabia
openaire   +2 more sources

Handling missing values in support vector machine classifiers

Neural Networks, 2005
This paper discusses the task of learning a classifier from observed data containing missing values amongst the inputs which are missing completely at random. A non-parametric perspective is adopted by defining a modified risk taking into account the uncertainty of the predicted outputs when missing values are involved.
Kristiaan Pelckmans, Jos De Brabanter, Johan A. K. Suykens, Bart De Moor   +3 more
openaire   +3 more sources

An Adaptive Ensemble Classifier for Handling Recurring Concepts

2019 International Multidisciplinary Information Technology and Engineering Conference (IMITEC), 2019
The assumption with many learning algorithms is that the underlying distribution of the data is static. However, for many real world applications, data is streaming and collected over an extended period of time. Learning in such dynamic and nonstationary environments presents a challenge not common in static domains as the statistical properties of the
Tinofirei Museba, Fulufhelo Nelwamodo, Khmaies Ouhada   +2 more
openaire   +1 more source

Handling missing features in maximum margin Bayesian network classifiers

2012 IEEE International Workshop on Machine Learning for Signal Processing, 2012
The Comprehensive Nuclear-Test-Ban Treaty Organization (CTBTO) records hydroacoustic data to detect nuclear explosions1. This enables verification of the Comprehensive Nuclear-Test-Ban Treaty once it has entered into force. The detection can be considered as a classification problem discriminating noise-like, earthquake-caused and explosion-like data ...
Sebastian Tschiatschek, Nikolaus Mutsam, Franz Pernkopf   +2 more
openaire   +1 more source