Search results for "UML"
showing 10 items of 407 documents
Classification Similarity Learning Using Feature-Based and Distance-Based Representations: A Comparative Study
2015
Automatically measuring the similarity between a pair of objects is a common and important task in the machine learning and pattern recognition fields. Being an object of study for decades, it has lately received an increasing interest from the scientific community. Usually, the proposed solutions have used either a feature-based or a distance-based representation to perform learning and classification tasks. This article presents the results of a comparative experimental study between these two approaches for computing similarity scores using a classification-based method. In particular, we use the Support Vector Machine as a flexible combiner both for a high dimensional feature space and …
A one class KNN for signal identification: a biological case study
2009
The paper describes an application of a one class KNN to identify different signal patterns embedded in a noise structured background. The problem becomes harder whenever only one pattern is well-represented in the signal; in such cases, one class classifier techniques are more indicated. The classification phase is applied after a preprocessing phase based on a multi layer model (MLM) that provides preliminary signal segmentation in an interval feature space. The one class KNN has been tested on synthetic and real (Saccharomyces cerevisiae) microarray data in the specific problem of DNA nucleosome and linker regions identification. Results have shown, in both cases, a good recognition rate.
Applying Wavelet Packet Decomposition and One-Class Support Vector Machine on Vehicle Acceleration Traces for Road Anomaly Detection
2013
Road condition monitoring through real-time intelligent systems has become more and more significant due to heavy road transportation. Road conditions can be roughly divided into normal and anomaly segments. The number of former should be much larger than the latter for a useable road. Based on the nature of road condition monitoring, anomaly detection is applied, especially for pothole detection in this study, using accelerometer data of a riding car. Accelerometer data were first labeled and segmented, after which features were extracted by wavelet packet decomposition. A classification model was built using one-class support vector machine. For the classifier, the data of some normal seg…
A kernel support vector machine based technique for Crohnâs disease classification in human patients
2017
In this paper a new technique for classification of patients affected by Crohnâs disease (CD) is proposed. The proposed technique is based on a Kernel Support Vector Machine (KSVM) and it adopts a Stratified K-Fold Cross-Validation strategy to enhance the KSVM classifier reliability. Traditional manual classification methods require radiological expertise and they usually are very time-consuming. Accordingly to three expert radiologists, a dataset composed of 300 patients has been selected for KSVM training and validation. Each patient was codified by 22 extracted qualitative features and classified as Positive or Negative as the related histological specimen result showed the CD. The eff…
A comparative study of best spectral bands selection systems for face recognition
2014
Multispectral images (MI) have shown promising capabilities to solve problems resulting from high illumination variation in face recognition. However, the use of MI, with the huge number of captured spectral bands for each subject, is impractical unless a system for best spectral bands selection (BSBS) is used. In this work, first we give an up to date overview of the existing BSBS techniques proposed for face recognition. We aim to highlight the imporatnce of this component of MI based systems. The reviewed techniques are then experimented using the multispectral face database IRIS - M3 to compare their performances. To the best of our knowledge this is the first study that reviews and com…
Intelligent system for material quality control using impact-echo testing
2008
This paper introduces an intelligent system to discern the quality of materials inspected by the impact-echo technique. The system includes a hardware setup to inspect parallelepiped-shape materials and a procedure to classify the material depending on its quality condition. Four levels of classification with different grades of knowledge about the material defects are approached: material condition, kind of defect, defect orientation, and defect dimension. The number of classes (material qualities) in the lowest classification level is 12. The procedure is applied on signals coming from 3D finite element simulations and lab experiments with aluminium specimens. The classification procedure…
<title>Dynamic integration of multiple data mining techniques in a knowledge discovery management system</title>
1999
One of the most important directions in improvement of data mining and knowledge discovery, is the integration of multiple classification techniques of an ensemble of classifiers. An integration technique should be able to estimate and select the most appropriate component classifiers from the ensemble. We present two variations of an advanced dynamic integration technique with two distance metrics. The technique is one variation of the stacked generalization method, with an assumption that each of the component classifiers is the best one, inside a certain sub area of the entire domain area. Our technique includes two phases: the learning phase and the application phase. During the learnin…
Interactive Pansharpening and Active Classification in Remote Sensing
2013
This chapter presents two multimodal prototypes for remote sensing image classification where user interaction is an important part of the system. The first one applies pansharpening techniques to fuse a panchromatic image and a multispectral image of the same scene to obtain a high resolution (HR) multispectral image. Once the HR image has been classified the user can interact with the system to select a class of interest. The pansharpening parameters are then modified to increase the system accuracy for the selected class without deteriorating the performance of the classifier on the other classes. The second prototype utilizes Bayesian modeling and inference to implement active learning …
Concept Drift Detection Using Online Histogram-Based Bayesian Classifiers
2016
In this paper, we present a novel algorithm that performs online histogram-based classification, i.e., specifically designed for the case when the data is dynamic and its distribution is non-stationary. Our method, called the Online Histogram-based Naïve Bayes Classifier (OHNBC) involves a statistical classifier based on the well-established Bayesian theory, but which makes some assumptions with respect to the independence of the attributes. Moreover, this classifier generates a prediction model using uni-dimensional histograms, whose segments or buckets are fixed in terms of their cardinalities but dynamic in terms of their widths. Additionally, our algorithm invokes the principles of info…
Online Estimation of Discrete Densities
2013
We address the problem of estimating a discrete joint density online, that is, the algorithm is only provided the current example and its current estimate. The proposed online estimator of discrete densities, EDDO (Estimation of Discrete Densities Online), uses classifier chains to model dependencies among features. Each classifier in the chain estimates the probability of one particular feature. Because a single chain may not provide a reliable estimate, we also consider ensembles of classifier chains and ensembles of weighted classifier chains. For all density estimators, we provide consistency proofs and propose algorithms to perform certain inference tasks. The empirical evaluation of t…