12,331 research outputs found
Impact of filter feature selection on classification: an empirical study
The high-dimensionality of Big Data poses challenges in data understanding and visualization. Furthermore, it leads to lengthy model building times in data analysis and poor generalization for machine learning models. Consequently, there is a need for feature selection, which allows identifying the more relevant part of the data to improve the data analysis (e.g., building simpler and more understandable models with reduced training time and improved model performance). This study aims to (i) characterize the factors (i.e., dataset characteristics) that influence the performance of feature selection methods, and (ii) assess the impact of feature selection on the training time and accuracy of binary and multiclass classification problems. As a result, we propose a systematic method to select representative datasets (i.e., considering the distributions of several dataset characteristics) in a given repository. Next, we provide an empirical study of the impact of eight feature selection methods on Naive Bayes (NB), Nearest Neighbor (KNN), Linear Discriminant Analysis (LDA), and Multilayer Perceptron (MLP) classification algorithms using 32 real-world datasets and a relative performance measure. We observed that feature selection is more effective in reducing training time (e.g., up to 60% for LDA classifiers) than improving classification accuracy (e.g., up to 5%). Furthermore, we observed that feature selection gives slight accuracy improvement for binary classification (i.e., up to 5%), while it mostly leads to accuracy degradation for multiclass classification. Although none of the studied feature selection methods is best in all cases, for multiclass classification, we observed that correlation based and minimum redundancy maximum relevance feature selection methods gave the best results in accuracy. Through statistical testing, we found LDA and MLP to benefit more in accuracy improvement after feature selection than KNN and NB.The project leading to this publication has received funding from the European Commission under the European Union’s Horizon 2020 research and innovation programme (grant agreement No 955895).Peer ReviewedPostprint (published version
The Construction of Support Vector Machine Classifier Using the Firefly Algorithm
The setting of parameters in the support vector machines (SVMs) is very important with regard to its accuracy and efficiency. In this paper, we employ the firefly algorithm to train all parameters of the SVM simultaneously, including the penalty parameter, smoothness parameter, and Lagrangian multiplier. The proposed method is called the firefly-based SVM (firefly-SVM). This tool is not considered the feature selection, because the SVM, together with feature selection, is not suitable for the application in a multiclass classification, especially for the one-against-all multiclass SVM. In experiments, binary and multiclass classifications are explored. In the experiments on binary classification, ten of the benchmark data sets of the University of California, Irvine (UCI), machine learning repository are used; additionally the firefly-SVM is applied to the multiclass diagnosis of ultrasonic supraspinatus images. The classification performance of firefly-SVM is also compared to the original LIBSVM method associated with the grid search method and the particle swarm optimization based SVM (PSO-SVM). The experimental results advocate the use of firefly-SVM to classify pattern classifications for maximum accuracy
A Fast Two-Stage Classification Method of Support Vector Machines
Classification of high-dimensional data generally requires enormous processing time. In this paper, we present a fast two-stage method of support vector machines, which includes a feature reduction algorithm and a fast multiclass method. First, principal component analysis is applied to the data for feature reduction and decorrelation, and then a feature selection method is used to further reduce feature dimensionality. The criterion based on Bhattacharyya distance is revised to get rid of influence of some binary problems with large distance. Moreover, a simple method is proposed to reduce the processing time of multiclass problems, where one binary SVM with the fewest support vectors (SVs) will be selected iteratively to exclude the less similar class until the final result is obtained. Experimented with the hyperspectral data 92AV3C, the results demonstrate that the proposed method can achieve a much faster classification and preserve the high classification accuracy of SVMs
Multi-Objective Genetic Algorithm for Multi-View Feature Selection
Multi-view datasets offer diverse forms of data that can enhance prediction
models by providing complementary information. However, the use of multi-view
data leads to an increase in high-dimensional data, which poses significant
challenges for the prediction models that can lead to poor generalization.
Therefore, relevant feature selection from multi-view datasets is important as
it not only addresses the poor generalization but also enhances the
interpretability of the models. Despite the success of traditional feature
selection methods, they have limitations in leveraging intrinsic information
across modalities, lacking generalizability, and being tailored to specific
classification tasks. We propose a novel genetic algorithm strategy to overcome
these limitations of traditional feature selection methods for multi-view data.
Our proposed approach, called the multi-view multi-objective feature selection
genetic algorithm (MMFS-GA), simultaneously selects the optimal subset of
features within a view and between views under a unified framework. The MMFS-GA
framework demonstrates superior performance and interpretability for feature
selection on multi-view datasets in both binary and multiclass classification
tasks. The results of our evaluations on three benchmark datasets, including
synthetic and real data, show improvement over the best baseline methods. This
work provides a promising solution for multi-view feature selection and opens
up new possibilities for further research in multi-view datasets
Mental distress detection and triage in forum posts: the LT3 CLPsych 2016 shared task system
This paper describes the contribution of LT3 for the CLPsych 2016 Shared Task on automatic triage of mental health forum posts. Our systems use multiclass Support Vector Machines (SVM), cascaded binary SVMs and ensembles with a rich feature set. The best systems obtain macro-averaged F-scores of 40% on the full task and 80% on the green versus alarming distinction. Multiclass SVMs with all features score best in terms of F-score, whereas feature filtering with bi-normal separation and classifier ensembling are found to improve recall of alarming posts
Scalable Solutions for Automated Single Pulse Identification and Classification in Radio Astronomy
Data collection for scientific applications is increasing exponentially and
is forecasted to soon reach peta- and exabyte scales. Applications which
process and analyze scientific data must be scalable and focus on execution
performance to keep pace. In the field of radio astronomy, in addition to
increasingly large datasets, tasks such as the identification of transient
radio signals from extrasolar sources are computationally expensive. We present
a scalable approach to radio pulsar detection written in Scala that
parallelizes candidate identification to take advantage of in-memory task
processing using Apache Spark on a YARN distributed system. Furthermore, we
introduce a novel automated multiclass supervised machine learning technique
that we combine with feature selection to reduce the time required for
candidate classification. Experimental testing on a Beowulf cluster with 15
data nodes shows that the parallel implementation of the identification
algorithm offers a speedup of up to 5X that of a similar multithreaded
implementation. Further, we show that the combination of automated multiclass
classification and feature selection speeds up the execution performance of the
RandomForest machine learning algorithm by an average of 54% with less than a
2% average reduction in the algorithm's ability to correctly classify pulsars.
The generalizability of these results is demonstrated by using two real-world
radio astronomy data sets.Comment: In Proceedings of the 47th International Conference on Parallel
Processing (ICPP 2018). ACM, New York, NY, USA, Article 11, 11 page
Radar-based Feature Design and Multiclass Classification for Road User Recognition
The classification of individual traffic participants is a complex task,
especially for challenging scenarios with multiple road users or under bad
weather conditions. Radar sensors provide an - with respect to well established
camera systems - orthogonal way of measuring such scenes. In order to gain
accurate classification results, 50 different features are extracted from the
measurement data and tested on their performance. From these features a
suitable subset is chosen and passed to random forest and long short-term
memory (LSTM) classifiers to obtain class predictions for the radar input.
Moreover, it is shown why data imbalance is an inherent problem in automotive
radar classification when the dataset is not sufficiently large. To overcome
this issue, classifier binarization is used among other techniques in order to
better account for underrepresented classes. A new method to couple the
resulting probabilities is proposed and compared to others with great success.
Final results show substantial improvements when compared to ordinary
multiclass classificationComment: 8 pages, 6 figure
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