Instance Selection with Neural Networks for Regression Problems

Kordos, Mirosław; Blachnik, Marcin

doi:10.1007/978-3-642-33266-1_33

Cited by 22 publications

(23 citation statements)

References 11 publications

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“…Recent methods in the field first solve other problems and then use classification, namely, regression problems as in [25][26][27][28][29][30], instance selection in data streams as in [31][32][33], and time series classification [34,35], or build ensembles of instance selection [36][37][38][39][40] and even create meta-learning systems, which automatically adjust a proper instance selection method to a given dataset as in [41,42].…”

Section: The Instance Selection Methodsmentioning

confidence: 99%

Instance Selection for Classifier Performance Estimation in Meta Learning

Blachnik

2017

Entropy

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Abstract:Building an accurate prediction model is challenging and requires appropriate model selection. This process is very time consuming but can be accelerated with meta-learning-automatic model recommendation by estimating the performances of given prediction models without training them. Meta-learning utilizes metadata extracted from the dataset to effectively estimate the accuracy of the model in question. To achieve that goal, metadata descriptors must be gathered efficiently and must be informative to allow the precise estimation of prediction accuracy. In this paper, a new type of metadata descriptors is analyzed. These descriptors are based on the compression level obtained from the instance selection methods at the data-preprocessing stage. To verify their suitability, two types of experiments on real-world datasets have been conducted. In the first one, 11 instance selection methods were examined in order to validate the compression-accuracy relation for three classifiers: k-nearest neighbors (kNN), support vector machine (SVM), and random forest. From this analysis, two methods are recommended (instance-based learning type 2 (IB2), and edited nearest neighbor (ENN)) which are then compared with the state-of-the-art metaset descriptors. The obtained results confirm that the two suggested compression-based meta-features help to predict accuracy of the base model much more accurately than the state-of-the-art solution.

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Section: The Instance Selection Methodsmentioning

confidence: 99%

Instance Selection for Classifier Performance Estimation in Meta Learning

Blachnik

2017

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“…Recently, researchers have used sample selection techniques, which were mainly applied to classification problems, for regression tasks. In [34,35], the authors proposed algorithms which used modified versions of Edited Nearest Neighbor (ENN [36]), Condensed Nearest Neighbor (CNN [37]), and CA [38] for sample selection for regression. Another sample selection method, Class Conditional Instance Selection (CCIS [39]), was modified for regression tasks in [40] and was applied for reducing variance in Genetic Fuzzy Systems (GFSs).…”

Section: Related Workmentioning

confidence: 99%

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

Abdulali

Hassan

Jeon

2016

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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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“…It extends the Class Conditional Instance Selection method for classification, which uses a class conditional nearest neighbour relation to guide the search process. The authors in [9] proposed the Threshold Condensed Nearest Neighbor (TCNN) and Threshold Edited Nearest Neighbor (TENN) algorithms-regression versions of the ENN and CNN methods for classification, respectively. These algorithms will be discussed in the next section, as they are used in this paper.…”

Section: Related Workmentioning

confidence: 99%

“…The Threshold Edited Nearest Neighbor (TENN) and Threshold Condensed Nearest Neighbor (TCNN) [9] adapt instance selection algorithms for classification problems-ENN [18] and CNN [8]-to the regression domain. They are presented in Algorithms 1 and 2.…”

Section: Pre-processing Strategiesmentioning

confidence: 99%

“…In the first case, we use the methods Threshold Condensed Nearest Neighbor (TCNN) and Threshold Edited Nearest Neighbor (TENN) [9] to select instances, which are then given to GSGP. The second approach incorporates instance selection to the evolution of GSGP, through the proposed Probabilistic instance Selection based on the Error (PSE) method.…”

Section: Introductionmentioning

confidence: 99%

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Reducing Dimensionality to Improve Search in Semantic Genetic Programming

Oliveira

Miranda

Pappa

et al. 2016

Parallel Problem Solving From Nature – PPSN XIV

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Abstract. Genetic programming approaches are moving from analysing the syntax of individual solutions to look into their semantics. One of the common definitions of the semantic space in the context of symbolic regression is a n-dimensional space, where n corresponds to the number of training examples. In problems where this number is high, the search process can became harder as the number of dimensions increase. Geometric semantic genetic programming (GSGP) explores the semantic space by performing geometric semantic operations-the fitness landscape seen by GSGP is guaranteed to be conic by construction. Intuitively, a lower number of dimensions can make search more feasible in this scenario, decreasing the chances of data overfitting and reducing the number of evaluations required to find a suitable solution. This paper proposes two approaches for dimensionality reduction in GSGP: (i) to apply current instance selection methods as a pre-process step before training points are given to GSGP; (ii) to incorporate instance selection to the evolution of GSGP. Experiments in 15 datasets show that GSGP performance is improved by using instance reduction during the evolution.

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Instance Selection with Neural Networks for Regression Problems

Cited by 22 publications

References 11 publications

Instance Selection for Classifier Performance Estimation in Meta Learning

Instance Selection for Classifier Performance Estimation in Meta Learning

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

Reducing Dimensionality to Improve Search in Semantic Genetic Programming

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