3,701 research outputs found
Local feature weighting in nearest prototype classification
The distance metric is the corner stone of nearest neighbor (NN)-based methods, and therefore, of nearest prototype (NP) algorithms. That is because they classify depending on the similarity of the data. When the data is characterized by a set of features which may contribute to the classification task in different levels, feature weighting or selection is required, sometimes in a local sense. However, local weighting is typically restricted to NN approaches. In this paper, we introduce local feature weighting (LFW) in NP classification. LFW provides each prototype its own weight vector, opposite to typical global weighting methods found in the NP literature, where all the prototypes share the same one. Providing each prototype its own weight vector has a novel effect in the borders of the Voronoi regions generated: They become nonlinear. We have integrated LFW with a previously developed evolutionary nearest prototype classifier (ENPC). The experiments performed both in artificial and real data sets demonstrate that the resulting algorithm that we call LFW in nearest prototype classification (LFW-NPC) avoids overfitting on training data in domains where the features may have different contribution to the classification task in different areas of the feature space. This generalization capability is also reflected in automatically obtaining an accurate and reduced set of prototypes.Publicad
Gray Image extraction using Fuzzy Logic
Fuzzy systems concern fundamental methodology to represent and process
uncertainty and imprecision in the linguistic information. The fuzzy systems
that use fuzzy rules to represent the domain knowledge of the problem are known
as Fuzzy Rule Base Systems (FRBS). On the other hand image segmentation and
subsequent extraction from a noise-affected background, with the help of
various soft computing methods, are relatively new and quite popular due to
various reasons. These methods include various Artificial Neural Network (ANN)
models (primarily supervised in nature), Genetic Algorithm (GA) based
techniques, intensity histogram based methods etc. providing an extraction
solution working in unsupervised mode happens to be even more interesting
problem. Literature suggests that effort in this respect appears to be quite
rudimentary. In the present article, we propose a fuzzy rule guided novel
technique that is functional devoid of any external intervention during
execution. Experimental results suggest that this approach is an efficient one
in comparison to different other techniques extensively addressed in
literature. In order to justify the supremacy of performance of our proposed
technique in respect of its competitors, we take recourse to effective metrics
like Mean Squared Error (MSE), Mean Absolute Error (MAE), Peak Signal to Noise
Ratio (PSNR).Comment: 8 pages, 5 figures, Fuzzy Rule Base, Image Extraction, Fuzzy
Inference System (FIS), Membership Functions, Membership values,Image coding
and Processing, Soft Computing, Computer Vision Accepted and published in
IEEE. arXiv admin note: text overlap with arXiv:1206.363
Two generalizations of Kohonen clustering
The relationship between the sequential hard c-means (SHCM), learning vector quantization (LVQ), and fuzzy c-means (FCM) clustering algorithms is discussed. LVQ and SHCM suffer from several major problems. For example, they depend heavily on initialization. If the initial values of the cluster centers are outside the convex hull of the input data, such algorithms, even if they terminate, may not produce meaningful results in terms of prototypes for cluster representation. This is due in part to the fact that they update only the winning prototype for every input vector. The impact and interaction of these two families with Kohonen's self-organizing feature mapping (SOFM), which is not a clustering method, but which often leads ideas to clustering algorithms is discussed. Then two generalizations of LVQ that are explicitly designed as clustering algorithms are presented; these algorithms are referred to as generalized LVQ = GLVQ; and fuzzy LVQ = FLVQ. Learning rules are derived to optimize an objective function whose goal is to produce 'good clusters'. GLVQ/FLVQ (may) update every node in the clustering net for each input vector. Neither GLVQ nor FLVQ depends upon a choice for the update neighborhood or learning rate distribution - these are taken care of automatically. Segmentation of a gray tone image is used as a typical application of these algorithms to illustrate the performance of GLVQ/FLVQ
A new fuzzy set merging technique using inclusion-based fuzzy clustering
This paper proposes a new method of merging parameterized fuzzy sets based on clustering in the parameters space, taking into account the degree of inclusion of each fuzzy set in the cluster prototypes. The merger method is applied to fuzzy rule base simplification by automatically replacing the fuzzy sets corresponding to a given cluster with that pertaining to cluster prototype. The feasibility and the performance of the proposed method are studied using an application in mobile robot navigation. The results indicate that the proposed merging and rule base simplification approach leads to good navigation performance in the application considered and to fuzzy models that are interpretable by experts. In this paper, we concentrate mainly on fuzzy systems with Gaussian membership functions, but the general approach can also be applied to other parameterized fuzzy sets
An MS Windows prototype for automatic general purpose image-based flaw detection
Flaw detection plays a crucial role in many industries to make sure that the products meet the specified quality requirements. When making for example a car it is important that all the parts satisfy certain quality standards to make sure the consumer buys a car that is safe to operate. A crack or another weakness in a crucial part can be catastrophic. To make sure their cars are as safe as possible, car manufacturers are conducting thorough testing of crucial parts. Similar tests are done in a wide variety of industries, and these quality controls are often referred to as flaw detection. Any cracks, voids, or other weaknesses that can cause danger are called flaws. Flaw detection is often done, or preferred done, in real time-- in an assembly line fashion. An important constraint, in addition to reliability, is therefore speed. The techniques used in these tests varies. Common techn~ques are ultrasonic waves (1-D or 2-D), eddy current imaging, x-ray imaging, thermal imaging, and fluorescent penetrent imaging. In this thesis I will discuss automatic general purpose image-based flaw detection. Automatic means that the flaw detection is performed without human supervision, and general purpose means that the inspection is not tailored to a specific task (i.e. one particular flaw in one particular type of object), but is ideally applicable to any detection problem
Land cover classification using fuzzy rules and aggregation of contextual information through evidence theory
Land cover classification using multispectral satellite image is a very
challenging task with numerous practical applications. We propose a multi-stage
classifier that involves fuzzy rule extraction from the training data and then
generation of a possibilistic label vector for each pixel using the fuzzy rule
base. To exploit the spatial correlation of land cover types we propose four
different information aggregation methods which use the possibilistic class
label of a pixel and those of its eight spatial neighbors for making the final
classification decision. Three of the aggregation methods use Dempster-Shafer
theory of evidence while the remaining one is modeled after the fuzzy k-NN
rule. The proposed methods are tested with two benchmark seven channel
satellite images and the results are found to be quite satisfactory. They are
also compared with a Markov random field (MRF) model-based contextual
classification method and found to perform consistently better.Comment: 14 pages, 2 figure
Radio Galaxy Zoo: Knowledge Transfer Using Rotationally Invariant Self-Organising Maps
With the advent of large scale surveys the manual analysis and classification
of individual radio source morphologies is rendered impossible as existing
approaches do not scale. The analysis of complex morphological features in the
spatial domain is a particularly important task. Here we discuss the challenges
of transferring crowdsourced labels obtained from the Radio Galaxy Zoo project
and introduce a proper transfer mechanism via quantile random forest
regression. By using parallelized rotation and flipping invariant Kohonen-maps,
image cubes of Radio Galaxy Zoo selected galaxies formed from the FIRST radio
continuum and WISE infrared all sky surveys are first projected down to a
two-dimensional embedding in an unsupervised way. This embedding can be seen as
a discretised space of shapes with the coordinates reflecting morphological
features as expressed by the automatically derived prototypes. We find that
these prototypes have reconstructed physically meaningful processes across two
channel images at radio and infrared wavelengths in an unsupervised manner. In
the second step, images are compared with those prototypes to create a
heat-map, which is the morphological fingerprint of each object and the basis
for transferring the user generated labels. These heat-maps have reduced the
feature space by a factor of 248 and are able to be used as the basis for
subsequent ML methods. Using an ensemble of decision trees we achieve upwards
of 85.7% and 80.7% accuracy when predicting the number of components and peaks
in an image, respectively, using these heat-maps. We also question the
currently used discrete classification schema and introduce a continuous scale
that better reflects the uncertainty in transition between two classes, caused
by sensitivity and resolution limits
Mining Extremes through Fuzzy Clustering
Archetypes are extreme points that synthesize data representing "pure" individual types.
Archetypes are assigned by the most discriminating features of data points, and are almost
always useful in applications when one is interested in extremes and not on commonalities.
Recent applications include talent analysis in sports and science, fraud detection,
profiling of users and products in recommendation systems, climate extremes, as well as
other machine learning applications.
The furthest-sum Archetypal Analysis (FS-AA) (Mørup and Hansen, 2012) and the
Fuzzy Clustering with Proportional Membership (FCPM) (Nascimento, 2005) propose
distinct models to find clusters with extreme prototypes. Even though the FCPM model
does not impose its prototypes to lie in the convex hull of data, it belongs to the framework
of data recovery from clustering (Mirkin, 2005), a powerful property for unsupervised
cluster analysis. The baseline version of FCPM, FCPM-0, provides central prototypes
whereas its smooth version, FCPM-2 provides extreme prototypes as AA archetypes.
The comparative study between FS-AA and FCPM algorithms conducted in this dissertation
covers the following aspects. First, the analysis of FS-AA on data recovery from
clustering using a collection of 100 data sets of diverse dimensionalities, generated with
a proper data generator (FCPM-DG) as well as 14 real world data. Second, testing the
robustness of the clustering algorithms in the presence of outliers, with the peculiar behaviour
of FCPM-0 on removing the proper number of prototypes from data. Third, a
collection of five popular fuzzy validation indices are explored on accessing the quality
of clustering results. Forth, the algorithms undergo a study to evaluate how different
initializations affect their convergence as well as the quality of the clustering partitions.
The Iterative Anomalous Pattern (IAP) algorithm allows to improve the convergence of
FCPM algorithm as well as to fine-tune the level of resolution to look at clustering results,
which is an advantage from FS-AA. Proper visualization functionalities for FS-AA and
FCPM support the easy interpretation of the clustering results
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