Learning to detect representative data for large scale instance selection

Lin, Wei; Tsai, Chih‐Fong; Ke, Shih Wen; Hung, Chia Wen; Eberle, William

doi:10.1016/j.jss.2015.04.038

Cited by 28 publications

(9 citation statements)

References 12 publications

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“…They have been widely used as the baseline instance selection algorithms in related studies [18, 23]. There are nine different combinations of instance selection and imputation methods for each dataset: IB3/DROP3/GA + KNNI, IB3/DROP3/GA + MLP, and IB3/DROP3/GA + SVM.…”

Section: The Two Imputation Processesmentioning

confidence: 99%

Outlier Removal in Model-Based Missing Value Imputation for Medical Datasets

Huang,

Lin,

Tsai

2018

Journal of Healthcare Engineering

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Many real-world medical datasets contain some proportion of missing (attribute) values. In general, missing value imputation can be performed to solve this problem, which is to provide estimations for the missing values by a reasoning process based on the (complete) observed data. However, if the observed data contain some noisy information or outliers, the estimations of the missing values may not be reliable or may even be quite different from the real values. The aim of this paper is to examine whether a combination of instance selection from the observed data and missing value imputation offers better performance than performing missing value imputation alone. In particular, three instance selection algorithms, DROP3, GA, and IB3, and three imputation algorithms, KNNI, MLP, and SVM, are used in order to find out the best combination. The experimental results show that that performing instance selection can have a positive impact on missing value imputation over the numerical data type of medical datasets, and specific combinations of instance selection and imputation methods can improve the imputation results over the mixed data type of medical datasets. However, instance selection does not have a definitely positive impact on the imputation result for categorical medical datasets.

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Section: The Two Imputation Processesmentioning

confidence: 99%

Outlier Removal in Model-Based Missing Value Imputation for Medical Datasets

Huang,

Lin,

Tsai

2018

Journal of Healthcare Engineering

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“…However, sample selection techniques find most usage in the field of data mining. The authors in [25,26] used scalable sample selection algorithms for dealing with very large scale datasets. Furthermore, [27] also provides a data condensation algorithm for large datasets in machine learning.…”

Section: Related Workmentioning

confidence: 99%

Stimuli-Magnitude-Adaptive Sample Selection for Data-Driven Haptic Modeling

Abdulali

Hassan

Jeon

2016

Entropy

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Data-driven haptic modeling is an emerging technique where contact dynamics are simulated and interpolated based on a generic input-output matching model identified by data sensed from interaction with target physical objects. In data-driven modeling, selecting representative samples from a large set of data in a way that they can efficiently and accurately describe the whole dataset has been a long standing problem. This paper presents a new algorithm for the sample selection where the variances of output are observed for selecting representative input-output samples in order to ensure the quality of output prediction. The main idea is that representative pairs of input-output are chosen so that the ratio of the standard deviation to the mean of the corresponding output group does not exceed an application-dependent threshold. This output-and standard deviation-based sample selection is very effective in applications where the variance or relative error of the output should be kept within a certain threshold. This threshold is used for partitioning the input space using Binary Space Partitioning-tree (BSP-tree) and k-means algorithms. We apply the new approach to data-driven haptic modeling scenario where the relative error of the output prediction result should be less than a perceptual threshold. For evaluation, the proposed algorithm is compared to two state-of-the-art sample selection algorithms for regression tasks. Four kinds of haptic related behavior-force datasets are tested. The results showed that the proposed algorithm outperformed the others in terms of output-approximation quality and computational complexity.

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“…Selection of important data from a big data bank, especially when nonstationary, combined of both old and new data samples, is a very critical problem due to computational complexity. In this context, Lin et al [19] proposed a representative data detection methodology based on pattern recognition techniques. Liu et al [20] proposed a new methodology using the centroid and its distance from samples to get the geometrical center of the class from labeled datasets without drastically demeaning the accuracy of SVM classification.…”

Section: Introductionmentioning

confidence: 99%

A multiseed-based SVM classification technique for training sample reduction

Sharif¹,

Chaudhuri²

2019

Turk J Elec Eng & Comp Sci

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A support vector machine (SVM) is not a popular method for a very large dataset classification because the training and testing time for such data are computationally expensive. Many researchers try to reduce the training time of SVMs by applying sample reduction methods. Many methods reduced the training samples by using a clustering technique. To reduce its high computational complexity, several data reduction methods were proposed in previous studies. However, such methods are not effective to extract informative patterns. This paper demonstrates a new supervised classification method, multiseed-based SVM (MSB-SVM), which is particularly intended to deal with very large datasets for multiclass classification. The main contributions of the paper are (i) an efficient multiseed technique for selection of seed points from circular/elongated class training samples, (ii) adjacent class pair selection from the set of multiseeds by using the minimum spanning tree, and (iii) extraction of support vectors from class pair seed equivalent regions to manage multiclass classification problems without being computationally expensive. Experimental results on a variety of datasets showed better performance compared to other sample-reducing methods in terms of training and testing time. Traditional support vector machine (SVM) solution suffers from O(n 2 ) time complexity, which makes it impractical for very large datasets. Here, multiseed point technique depends on the estimated density of each data, and the order of computation is O(n log n) . Using the estimated density, the computational cost of the seed selection algorithm is O(n) . So, this is the only burden for reducing the sample. However, reducing the sample takes less time with the proposed algorithm compared to the clustering methods. At the same time, the number of support vectors has been abruptly reduced, which takes less time to find the decision surface. Apart from this, the classification accuracy of the proposed technique is significantly better than other existing sample reduction methods especially for large datasets.

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Learning to detect representative data for large scale instance selection

Cited by 28 publications

References 12 publications

Outlier Removal in Model-Based Missing Value Imputation for Medical Datasets

Outlier Removal in Model-Based Missing Value Imputation for Medical Datasets

Stimuli-Magnitude-Adaptive Sample Selection for Data-Driven Haptic Modeling

A multiseed-based SVM classification technique for training sample reduction

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