489,769 research outputs found
EFSIS: Ensemble Feature Selection Integrating Stability
Ensemble learning that can be used to combine the predictions from multiple
learners has been widely applied in pattern recognition, and has been reported
to be more robust and accurate than the individual learners. This ensemble
logic has recently also been more applied in feature selection. There are
basically two strategies for ensemble feature selection, namely data
perturbation and function perturbation. Data perturbation performs feature
selection on data subsets sampled from the original dataset and then selects
the features consistently ranked highly across those data subsets. This has
been found to improve both the stability of the selector and the prediction
accuracy for a classifier. Function perturbation frees the user from having to
decide on the most appropriate selector for any given situation and works by
aggregating multiple selectors. This has been found to maintain or improve
classification performance. Here we propose a framework, EFSIS, combining these
two strategies. Empirical results indicate that EFSIS gives both high
prediction accuracy and stability.Comment: 20 pages, 3 figure
An Open Source Pattern Recognition Toolbox for MATLAB
Pattern recognition and machine learning are becoming integral parts of
algorithms in a wide range of applications. Different algorithms and approaches
for machine learning include different tradeoffs between performance and
computation, so during algorithm development it is often necessary to explore a
variety of different approaches to a given task. A toolbox with a unified
framework across multiple pattern recognition techniques enables algorithm
developers the ability to rapidly evaluate different choices prior to
deployment. MATLAB is a widely used environment for algorithm development and
prototyping, and although several MATLAB toolboxes for pattern recognition are
currently available these are either incomplete, expensive, or restrictively
licensed. In this work we describe a MATLAB toolbox for pattern recognition and
machine learning known as the PRT (Pattern Recognition Toolbox), licensed under
the permissive MIT license. The PRT includes many popular techniques for data
preprocessing, supervised learning, clustering, regression and feature
selection, as well as a methodology for combining these components using a
simple, uniform syntax. The resulting algorithms can be evaluated using
cross-validation and a variety of scoring metrics to ensure robust performance
when the algorithm is deployed. This paper presents an overview of the PRT as
well as an example of usage on Fisher's Iris dataset
A new method for feature selection based on fuzzy similarity measures using multi objective genetic algorithm
Feature selection (FS) is considered to be an important preprocessing step in machine learning and pattern recognition, and feature evaluation is the key issue for constructing a feature selection algorithm. Feature selection process can also reduce noise and this way enhance the classification accuracy. In this article, feature selection method based on fuzzy similarity measures by multi objective genetic algorithm (FSFSM - MOGA) is introduced and performance of the proposed method on published data sets from UCI was evaluated. The results show the efficiency of the method is compared with the conventional version. When this method multi-objective genetic algorithms and fuzzy similarity measures used in CFS method can improve it
Feature fusion, feature selection and local n-ary patterns for object recognition and image classification
University of Technology Sydney. Faculty of Engineering and Information Technology.Object recognition is one of the most fundamental topics in computer vision. During past years, it has been the interest for both academies working in computer science and professionals working in the information technology (IT) industry. The popularity of object recognition has been proven by its motivation of sophisticated theories in science and wide spread applications in the industry. Nowadays, with more powerful machine learning tools (both hardware and software) and the huge amount of information (data) readily available, higher expectations are imposed on object recognition. At its early stage in the 1990s, the task of object recognition can be as simple as to differentiate between object of interest and non-object of interest from a single still image. Currently, the task of object recognition may as well includes the segmentation and labeling of different image regions (i.e., to assign each segmented image region a meaningful label based on objects appear in those regions), and then using computer programs to infer the scene of the overall image based on those segmented regions. The original two-class classification problem is now getting more complex as it now evolves toward a multi-class classification problem. In this thesis, contributions on object recognition are made in two aspects. These are, improvements using feature fusion and improvements using feature selection. Three examples are given in this thesis to illustrate three different feature fusion methods, the descriptor concatenation (the low-level fusion), the confidence value escalation (the mid-level fusion) and the coarse-to-fine framework (the high-level fusion). Two examples are provided for feature selection to demonstrate its ideas, those are, optimal descriptor selection and improved classifier selection.
Feature extraction plays a key role in object recognition because it is the first and also the most important step. If we consider the overall object recognition process, machine learning tools are to serve the purpose of finding distinctive features from the visual data. Given distinctive features, object recognition is readily available (e.g., a simple threshold function can be used to classify feature descriptors). The proposal of Local N-ary Pattern (LNP) texture features contributes to both feature extraction and texture classification. The distinctive LNP feature generalizes the texture feature extraction process and improves texture classification. Concretely, the local binary pattern (LBP) is the special case of LNP with n = 2 and the texture spectrum is the special case of LNP with n = 3. The proposed LNP representation has been proven to outperform the popular LBP and one of the LBP’s most successful extension - local ternary pattern (LTP) for texture classification
Simulation and classification of power quality events based on wavelet transform energy using feature selection method
In this paper mathematical modeling method has been used in order to simulating of power quality events. In regarding to excellent neural network function in classification and pattern recognition works, multilayer neural network has been used for events classification. The STFT and Discrete Wavelet Transform are used for feature extraction. Acquired features of this step are decreased using advance algorithm of feature selection. Then these extracted features were given to the neural network as input. The simulation results showed that the classification precision of data caused by the algorithm of feature selection is better in compare with the precision of total data classification.Keywords: Power quality, neural network, Wavelet transform, Feature selectio
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