共查询到10条相似文献,搜索用时 156 毫秒
1.
Ivan Bajla František Rublík Barbora Arendacká Igor Farkaš Klára Hornišová Svorad Štolc Viktor Witkovský 《Machine Vision and Applications》2009,20(4):243-259
A software system Gel Analysis System for Epo (GASepo) has been developed within an international WADA project. As recent
WADA criteria of rEpo positivity are based on identification of each relevant object (band) in Epo images, development of
suitable methods of image segmentation and object classification were needed for the GASepo system. In the paper we address
two particular problems: segmentation of disrupted bands and classification of the segmented objects into three or two classes.
A novel band projection operator is based on convenient object merging measures and their discrimination analysis using specifically
generated training set of segmented objects. A weighted ranks classification method is proposed, which is new in the field
of image classification. It is based on ranks of the values of a specific criterial function. The weighted ranks classifiers
proposed in our paper have been evaluated on real samples of segmented objects of Epo images and compared to three selected
well-known classifiers: Fisher linear classifier, Support Vector Machine, and Multilayer Perceptron.
相似文献
Svorad Štolc (Corresponding author)Email: |
2.
In this paper, we study the performance improvement that it is possible to obtain combining classifiers based on different
notions (each trained using a different physicochemical property of amino-acids). This multi-classifier has been tested in
three problems: HIV-protease; recognition of T-cell epitopes; predictive vaccinology. We propose a multi-classifier that combines
a classifier that approaches the problem as a two-class pattern recognition problem and a method based on a one-class classifier.
Several classifiers combined with the “sum rule” enables us to obtain an improvement performance over the best results previously
published in the literature.
相似文献
Loris NanniEmail: |
3.
Massimo De Santo Gennaro Percannella Carlo Sansone Mario Vento 《Pattern Analysis & Applications》2007,10(2):135-145
In this paper, we propose an innovative architecture to segment a news video into the so-called “stories” by both using the
included video and audio information. Segmentation of news into stories is one of the key issues for achieving efficient treatment
of news-based digital libraries. While the relevance of this research problem is widely recognized in the scientific community,
we are in presence of a few established solutions in the field. In our approach, the segmentation is performed in two steps:
first, shots are classified by combining three different anchor shot detection algorithms using video information only. Then,
the shot classification is improved by using a novel anchor shot detection method based on features extracted from the audio
track. Tests on a large database confirm that the proposed system outperforms each single video-based method as well as their
combination.
相似文献
Mario VentoEmail: |
4.
Themis P. Exarchos Markos G. Tsipouras Costas Papaloukas Dimitrios I. Fotiadis 《Knowledge and Information Systems》2009,19(2):249-264
In this paper we present a novel methodology for sequence classification, based on sequential pattern mining and optimization
algorithms. The proposed methodology automatically generates a sequence classification model, based on a two stage process.
In the first stage, a sequential pattern mining algorithm is applied to a set of sequences and the sequential patterns are
extracted. Then, the score of every pattern with respect to each sequence is calculated using a scoring function and the score
of each class under consideration is estimated by summing the specific pattern scores. Each score is updated, multiplied by
a weight and the output of the first stage is the classification confusion matrix of the sequences. In the second stage an
optimization technique, aims to finding a set of weights which minimize an objective function, defined using the classification
confusion matrix. The set of the extracted sequential patterns and the optimal weights of the classes comprise the sequence
classification model. Extensive evaluation of the methodology was carried out in the protein classification domain, by varying
the number of training and test sequences, the number of patterns and the number of classes. The methodology is compared with
other similar sequence classification approaches. The proposed methodology exhibits several advantages, such as automated
weight assignment to classes using optimization techniques and knowledge discovery in the domain of application.
相似文献
Dimitrios I. FotiadisEmail: |
5.
This paper presents a new approach to Particle Swarm Optimization, called Michigan Approach PSO (MPSO), and its application
to continuous classification problems as a Nearest Prototype (NP) classifier. In Nearest Prototype classifiers, a collection
of prototypes has to be found that accurately represents the input patterns. The classifier then assigns classes based on
the nearest prototype in this collection. The MPSO algorithm is used to process training data to find those prototypes. In
the MPSO algorithm each particle in a swarm represents a single prototype in the solution and it uses modified movement rules
with particle competition and cooperation that ensure particle diversity. The proposed method is tested both with artificial
problems and with real benchmark problems and compared with several algorithms of the same family. Results show that the particles
are able to recognize clusters, find decision boundaries and reach stable situations that also retain adaptation potential.
The MPSO algorithm is able to improve the accuracy of 1-NN classifiers, obtains results comparable to the best among other
classifiers, and improves the accuracy reported in literature for one of the problems.
相似文献
Pedro IsasiEmail: |
6.
Charles W. Fox Ben Mitchinson Martin J. Pearson Anthony G. Pipe Tony J. Prescott 《Autonomous Robots》2009,26(4):223-239
Actuated artificial whiskers modeled on rat macrovibrissae can provide effective tactile sensor systems for autonomous robots.
This article focuses on texture classification using artificial whiskers and addresses a limitation of previous studies, namely,
their use of whisker deflection signals obtained under relatively constrained experimental conditions. Here we consider the
classification of signals obtained from a whiskered robot required to explore different surface textures from a range of orientations
and distances. This procedure resulted in a variety of deflection signals for any given texture. Using a standard Gaussian
classifier we show, using both hand-picked features and ones derived from studies of rat vibrissal processing, that a robust
rough-smooth discrimination is achievable without any knowledge of how the whisker interacts with the investigated object.
On the other hand, finer discriminations appear to require knowledge of the target’s relative position and/or of the manner
in which the whisker contact its surface.
Electronic Supplementary Material The online version of this article () contains supplementary material, which is available to authorized users.
相似文献
Anthony G. Pipe |
7.
Face recognition based on a novel linear discriminant criterion 总被引:1,自引:0,他引:1
Fengxi Song David Zhang Qinglong Chen Jizhong Wang 《Pattern Analysis & Applications》2007,10(3):165-174
As an effective technique for feature extraction and pattern classification Fisher linear discriminant (FLD) has been successfully applied in many fields. However, for a task with very high-dimensional data such as face images,
conventional FLD technique encounters a fundamental difficulty caused by singular within-class scatter matrix. To avoid the
trouble, many improvements on the feature extraction aspect of FLD have been proposed. In contrast, studies on the pattern
classification aspect of FLD are quiet few. In this paper, we will focus our attention on the possible improvement on the
pattern classification aspect of FLD by presenting a novel linear discriminant criterion called maximum scatter difference (MSD). Theoretical analysis demonstrates that MSD criterion is a generalization of Fisher discriminant criterion, and is
the asymptotic form of discriminant criterion: large margin linear projection. The performance of MSD classifier is tested in face recognition. Experiments performed on the ORL, Yale, FERET and AR databases
show that MSD classifier can compete with top-performance linear classifiers such as linear support vector machines, and is better than or equivalent to combinations of well known facial feature extraction methods, such as eigenfaces, Fisherfaces, orthogonal complementary space, nullspace, direct linear discriminant analysis, and the nearest neighbor classifier.
相似文献
Fengxi SongEmail: |
8.
Learning decision tree for ranking 总被引:4,自引:3,他引:1
Decision tree is one of the most effective and widely used methods for classification. However, many real-world applications
require instances to be ranked by the probability of class membership. The area under the receiver operating characteristics
curve, simply AUC, has been recently used as a measure for ranking performance of learning algorithms. In this paper, we present
two novel class probability estimation algorithms to improve the ranking performance of decision tree. Instead of estimating
the probability of class membership using simple voting at the leaf where the test instance falls into, our algorithms use
similarity-weighted voting and naive Bayes. We design empirical experiments to verify that our new algorithms significantly
outperform the recent decision tree ranking algorithm C4.4 in terms of AUC.
相似文献
Liangxiao JiangEmail: |
9.
Adaptation and application of multi-objective evolutionary algorithms for rule reduction and parameter tuning of fuzzy rule-based systems 总被引:7,自引:7,他引:0
María José Gacto Rafael Alcalá Francisco Herrera 《Soft Computing - A Fusion of Foundations, Methodologies and Applications》2009,13(5):419-436
Recently, multi-objective evolutionary algorithms have been applied to improve the difficult tradeoff between interpretability
and accuracy of fuzzy rule-based systems. It is known that both requirements are usually contradictory, however, these kinds
of algorithms can obtain a set of solutions with different trade-offs. This contribution analyzes different application alternatives
in order to attain the desired accuracy/interpr-etability balance by maintaining the improved accuracy that a tuning of membership
functions could give but trying to obtain more compact models. In this way, we propose the use of multi-objective evolutionary
algorithms as a tool to get almost one improved solution with respect to a classic single objective approach (a solution that
could dominate the one obtained by such algorithm in terms of the system error and number of rules). To do that, this work
presents and analyzes the application of six different multi-objective evolutionary algorithms to obtain simpler and still
accurate linguistic fuzzy models by performing rule selection and a tuning of the membership functions. The results on two
different scenarios show that the use of expert knowledge in the algorithm design process significantly improves the search
ability of these algorithms and that they are able to improve both objectives together, obtaining more accurate and at the
same time simpler models with respect to the single objective based approach.
相似文献
María José Gacto (Corresponding author)Email: |
Rafael AlcaláEmail: |
Francisco HerreraEmail: |
10.
Mohammad M. Masud Latifur Khan Bhavani Thuraisingham 《Information Systems Frontiers》2008,10(1):33-45
We present a scalable and multi-level feature extraction technique to detect malicious executables. We propose a novel combination
of three different kinds of features at different levels of abstraction. These are binary n-grams, assembly instruction sequences, and Dynamic Link Library (DLL) function calls; extracted from binary executables,
disassembled executables, and executable headers, respectively. We also propose an efficient and scalable feature extraction
technique, and apply this technique on a large corpus of real benign and malicious executables. The above mentioned features
are extracted from the corpus data and a classifier is trained, which achieves high accuracy and low false positive rate in
detecting malicious executables. Our approach is knowledge-based because of several reasons. First, we apply the knowledge
obtained from the binary n-gram features to extract assembly instruction sequences using our Assembly Feature Retrieval algorithm. Second, we apply
the statistical knowledge obtained during feature extraction to select the best features, and to build a classification model.
Our model is compared against other feature-based approaches for malicious code detection, and found to be more efficient
in terms of detection accuracy and false alarm rate.
相似文献
Bhavani Thuraisingham (Corresponding author)Email: |