Genetic algorithm assisted by a SVM for feature selection in gait classification

Yeoh, TzeWei; Zapotecas-Martínez, Saúl; Akimoto, Youhei; Aguirre, Hernán; Tanaka, Kiyoshi

doi:10.1109/ispacs.2014.7024450

Cited by 10 publications

(5 citation statements)

References 21 publications

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“…But it narrows to simpler SVM if no missing data is present in the data. Further an analysis of Least square SVM [4] [24] is done to understand the classifier better.…”

Section: Related Workmentioning

confidence: 99%

“…The concept of using data dictionary also yields effective results. The parameter namely missing rate [4] is taken into account for evaluation.…”

Section: Related Workmentioning

confidence: 99%

“…The genetic algorithm creates an opportunity for global search hence improves global optimum through the available solutions. The genetic algorithm tries to identify the attributes with the missing values [4]. Once the attribute has been identified, it engages in finding domain values of missing data values.…”

Section: Introductionmentioning

confidence: 99%

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Missing Data Prediction using Correlation Genetic Algorithm and SVM Approach

Alhroob¹,

Alzyadat²,

Almukahel³

et al. 2020

IJACSA

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Data exists in large volume in the modern world, it becomes very useful when decoded correctly to inform decision making towards tackling real word issues. However, when the data is conflicting, it becomes a daunting task to get obtain information. Working on missing data has become a very important task in big data analysis. This paper considers the handling of the missing data using the Support Vector Machine (SVM) based on a technique called Correlation-Genetic Algorithm-SVM. This data is to be subjected to the SVM classification technique after identifying the attribute's correlation and application of the genetic algorithm. The application of the correlation enables a clear view of the attributes which are highly correlated within a particular dataset. The results indicate that apart from the SVM, the application of the proposed hybrid algorithm produces better outcomes identification rate and accuracy is considered. The proposed approach is also compared with depicts the Mean Identification rate of applying the neural network, the result indicate a consistent accuracy hence making it better.

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“…But it narrows to simpler SVM if no missing data is present in the data. Further an analysis of Least square SVM [4] [24] is done to understand the classifier better.…”

Section: Related Workmentioning

confidence: 99%

“…The concept of using data dictionary also yields effective results. The parameter namely missing rate [4] is taken into account for evaluation.…”

Section: Related Workmentioning

confidence: 99%

See 1 more Smart Citation

Missing Data Prediction using Correlation Genetic Algorithm and SVM Approach

Alhroob¹,

Alzyadat²,

Almukahel³

et al. 2020

IJACSA

View full text Add to dashboard Cite

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“…Jia et al [20] have shown how incorporating the head and shoulder mean shape (HSMS) along with the Lucas-Kanade variant of the gait flow image (GFI) [25] greatly improves recognition accuracy. The genetic algorithm [26] was previously used in [27] to optimize the selection of model-based gait parameters and also in [28] for the selection of superimposed contour features.…”

Section: Introductionmentioning

confidence: 99%

View-Invariant Gait Recognition Through Genetic Template Segmentation

Isaac

Elias

Rajagopalan

et al. 2017

IEEE Signal Process. Lett.

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Template-based model-free approach provides by far the most successful solution to the gait recognition problem in literature. Recent work discusses how isolating the head and leg portion of the template increase the performance of a gait recognition system making it robust against covariates like clothing and carrying conditions. However, most involve a manual definition of the boundaries. The method we propose, the genetic template segmentation (GTS), employs the genetic algorithm to automate the boundary selection process. This method was tested on the GEI, GEnI and AEI templates. GEI seems to exhibit the best result when segmented with our approach. Experimental results depict that our approach significantly outperforms the existing implementations of view-invariant gait recognition.

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“…As GP can handle large search spaces by providing automated solutions [167], it is promising to use GP as a search method for feature selection. Note that other EC techniques have also been used for feature selection by existing works: ant colony optimization (ACO) [31], genetic algorithm [102,175] and particle swarm intelligence [93]. As the focus of this thesis is to solve existing problems in figure-ground segmentation and improve the segmentation performance, e.g.…”

Section: Gp Potentials and Challenges For Feature Selectionmentioning

confidence: 99%

Genetic Programming for Supervised Figure-ground Image Segmentation

Liang¹

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<p>Figure-ground segmentation is a process of separating regions of interest from unimportant backgrounds. It is essential to various applications in computer vi- sion and image processing, e.g. object tracking and image editing, as they are only interested in certain regions of an image and use figure-ground segmenta- tion as a pre-processing step. Traditional figure-ground segmentation methods often require heavy human workload (e.g. ground truth labeling), and/or rely heavily on human guidance (e.g. locating an initial model), accordingly cannot easily adapt to diverse image domains. Evolutionary computation (EC) is a family of algorithms for global optimi- sation, which are inspired by biological evolution. As an EC technique, genetic programming (GP) can evolve algorithms automatically for complex problems without pre-defining solution models. Compared with other EC techniques, GP is more flexible as it can utilise complex and variable-length representations (e.g. trees) of candidate solutions. It is hypothesised that this flexibility of GP makes it possible to evolve better solutions than those designed by experts. However, there have been limited attempts at applying GP to figure-ground segmentation. In this thesis, GP is enabled to successfully address figure-ground segmentation through evolving well-performing segmentors and generating effective features. The objectives are to investigate various image features as inputs of GP, develop multi-objective approaches, develop feature selection/construction methods, and conduct further evaluations of the proposed GP methods. The following new methods have been developed. Effective terminal sets of GP are investigated for figure-ground segmentation, covering three general types of image features, i.e. colour/brightness, texture and shape features. Results show that texture features are more effective than intensities and shape features as they are discriminative for different materials that foreground and background regions normally belong to (e.g. metal or wood). Two new multi-objective GP methods are proposed to evolve figure-ground segmentors, aiming at producing solutions balanced between the segmentation performance and solution complexity. Compared with a reference method that does not consider complexity and a parsimony pressure based method (a popular bloat control technique), the proposed methods can significantly reduce the solution size while achieving similar segmentation performance based on the Mann- Whitney U-Test at the significance level 5%. GP is introduced for the first time to conduct feature selection for figure- ground segmentation tasks, aiming to maximise the segmentation performance and minimise the number of selected features. The proposed methods produce feature subsets that lead to solutions achieving better segmentation performance with lower features than those of two benchmark methods (i.e. sequential forward selection and sequential backward selection) and the original full feature set. This is due to GP’s high search ability and higher likelihood of finding the global optima. GP is introduced for the first time to construct high-level features from primitive image features, which aims to improve the image segmentation performance, especially on complex images. By considering linear/non-linear interactions of the original features, the proposed methods construct fewer features that achieve better segmentation performance than the original full feature set. This investigation has shown that GP is suited for figure-ground image segmentation for the following reasons. Firstly, the proposed methods can evolve segmentors with useful class characteristic patterns to segment various types of objects. Secondly, the segmentors evolved from one type of foreground object can generalise well on similar objects. Thirdly, both the selected and constructed features of the proposed GP methods are more effective than original features, with the selected/constructed features being better for subsequent tasks. Finally, compared with other segmentation techniques, the major strengths of GP are that it does not require pre-defined problem models, and can be easily adapted to diverse image domains without major parameter tuning or human intervention.</p>

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Genetic algorithm assisted by a SVM for feature selection in gait classification

Cited by 10 publications

References 21 publications

Missing Data Prediction using Correlation Genetic Algorithm and SVM Approach

Missing Data Prediction using Correlation Genetic Algorithm and SVM Approach

View-Invariant Gait Recognition Through Genetic Template Segmentation

Genetic Programming for Supervised Figure-ground Image Segmentation

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