12,749 research outputs found
Fuzzy Least Squares Twin Support Vector Machines
Least Squares Twin Support Vector Machine (LST-SVM) has been shown to be an
efficient and fast algorithm for binary classification. It combines the
operating principles of Least Squares SVM (LS-SVM) and Twin SVM (T-SVM); it
constructs two non-parallel hyperplanes (as in T-SVM) by solving two systems of
linear equations (as in LS-SVM). Despite its efficiency, LST-SVM is still
unable to cope with two features of real-world problems. First, in many
real-world applications, labels of samples are not deterministic; they come
naturally with their associated membership degrees. Second, samples in
real-world applications may not be equally important and their importance
degrees affect the classification. In this paper, we propose Fuzzy LST-SVM
(FLST-SVM) to deal with these two characteristics of real-world data. Two
models are introduced for FLST-SVM: the first model builds up crisp hyperplanes
using training samples and their corresponding membership degrees. The second
model, on the other hand, constructs fuzzy hyperplanes using training samples
and their membership degrees. Numerical evaluation of the proposed method with
synthetic and real datasets demonstrate significant improvement in the
classification accuracy of FLST-SVM when compared to well-known existing
versions of SVM
One-Class Classification: Taxonomy of Study and Review of Techniques
One-class classification (OCC) algorithms aim to build classification models
when the negative class is either absent, poorly sampled or not well defined.
This unique situation constrains the learning of efficient classifiers by
defining class boundary just with the knowledge of positive class. The OCC
problem has been considered and applied under many research themes, such as
outlier/novelty detection and concept learning. In this paper we present a
unified view of the general problem of OCC by presenting a taxonomy of study
for OCC problems, which is based on the availability of training data,
algorithms used and the application domains applied. We further delve into each
of the categories of the proposed taxonomy and present a comprehensive
literature review of the OCC algorithms, techniques and methodologies with a
focus on their significance, limitations and applications. We conclude our
paper by discussing some open research problems in the field of OCC and present
our vision for future research.Comment: 24 pages + 11 pages of references, 8 figure
Brain image clustering by wavelet energy and CBSSO optimization algorithm
Previously, the diagnosis of brain abnormality was significantly important in the saving of social and hospital resources. Wavelet energy is known as an effective feature detection which has great efficiency in different utilities. This paper suggests a new method based on wavelet energy to automatically classify magnetic resonance imaging (MRI) brain images into two groups (normal and abnormal), utilizing support vector machine (SVM) classification based on chaotic binary shark smell optimization (CBSSO) to optimize the SVM weights.
The results of the suggested CBSSO-based KSVM are compared favorably to several other methods in terms of better sensitivity and authenticity. The proposed CAD system can additionally be utilized to categorize the images with various pathological conditions, types, and illness modes
Modeling Financial Time Series with Artificial Neural Networks
Financial time series convey the decisions and actions of a population of human actors over time. Econometric and regressive models have been developed in the past decades for analyzing these time series. More recently, biologically inspired artificial neural network models have been shown to overcome some of the main challenges of traditional techniques by better exploiting the non-linear, non-stationary, and oscillatory nature of noisy, chaotic human interactions. This review paper explores the options, benefits, and weaknesses of the various forms of artificial neural networks as compared with regression techniques in the field of financial time series analysis.CELEST, a National Science Foundation Science of Learning Center (SBE-0354378); SyNAPSE program of the Defense Advanced Research Project Agency (HR001109-03-0001
- …