125 research outputs found
Adaptive imputation of missing values for incomplete pattern classification
In classification of incomplete pattern, the missing values can either play a
crucial role in the class determination, or have only little influence (or
eventually none) on the classification results according to the context. We
propose a credal classification method for incomplete pattern with adaptive
imputation of missing values based on belief function theory. At first, we try
to classify the object (incomplete pattern) based only on the available
attribute values. As underlying principle, we assume that the missing
information is not crucial for the classification if a specific class for the
object can be found using only the available information. In this case, the
object is committed to this particular class. However, if the object cannot be
classified without ambiguity, it means that the missing values play a main role
for achieving an accurate classification. In this case, the missing values will
be imputed based on the K-nearest neighbor (K-NN) and self-organizing map (SOM)
techniques, and the edited pattern with the imputation is then classified. The
(original or edited) pattern is respectively classified according to each
training class, and the classification results represented by basic belief
assignments are fused with proper combination rules for making the credal
classification. The object is allowed to belong with different masses of belief
to the specific classes and meta-classes (which are particular disjunctions of
several single classes). The credal classification captures well the
uncertainty and imprecision of classification, and reduces effectively the rate
of misclassifications thanks to the introduction of meta-classes. The
effectiveness of the proposed method with respect to other classical methods is
demonstrated based on several experiments using artificial and real data sets
Inexpensive fusion methods for enhancing feature detection
Recent successful approaches to high-level feature detection in image and video data have treated the problem as a pattern classification task. These typically leverage the techniques learned from statistical machine learning, coupled with ensemble architectures that create multiple feature detection models. Once created, co-occurrence between learned features can be captured to further boost performance. At multiple stages throughout these frameworks, various pieces of evidence can be fused together in order to boost performance. These approaches whilst very successful are computationally expensive, and depending on the task, require the use of significant computational resources. In this paper we propose two fusion methods that aim to combine the output of an initial basic statistical machine learning approach with a lower-quality information source, in order to gain diversity in the classified results whilst requiring only modest computing resources. Our approaches, validated experimentally on TRECVid data, are designed to be complementary to existing frameworks and can be regarded as possible replacements for the more computationally expensive combination strategies used elsewhere
Generalized Evidence Theory
Conflict management is still an open issue in the application of Dempster
Shafer evidence theory. A lot of works have been presented to address this
issue. In this paper, a new theory, called as generalized evidence theory
(GET), is proposed. Compared with existing methods, GET assumes that the
general situation is in open world due to the uncertainty and incomplete
knowledge. The conflicting evidence is handled under the framework of GET. It
is shown that the new theory can explain and deal with the conflicting evidence
in a more reasonable way.Comment: 39 pages, 5 figure
A novel multi-classifier information fusion based on Dempster-Shafer theory: application to vibration-based fault detection
Achieving a high prediction rate is a crucial task in fault detection.
Although various classification procedures are available, none of them can give
high accuracy in all applications. Therefore, in this paper, a novel
multi-classifier fusion approach is developed to boost the performance of the
individual classifiers. This is acquired by using Dempster-Shafer theory (DST).
However, in cases with conflicting evidences, the DST may give
counter-intuitive results. In this regard, a preprocessing technique based on a
new metric is devised in order to measure and mitigate the conflict between the
evidences. To evaluate and validate the effectiveness of the proposed approach,
the method is applied to 15 benchmarks datasets from UCI and KEEL. Further, it
is applied for classifying polycrystalline Nickel alloy first-stage turbine
blades based on their broadband vibrational response. Through statistical
analysis with different noise levels, and by comparing with four
state-of-the-art fusion techniques, it is shown that that the proposed method
improves the classification accuracy and outperforms the individual
classifiers.Comment: arXiv admin note: text overlap with arXiv:2007.0878
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