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1

Ensembles of instance selection methods: A comparative study

Blachnik Marcin

International Journal of Applied Mathematics and Computer Science

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2019

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Vol. 29, no. 1

151--168

EN

Instance selection is often performed as one of the preprocessing methods which, along with feature selection, allows a significant reduction in computational complexity and an increase in prediction accuracy. So far, only few authors have considered ensembles of instance selection methods, while the ensembles of final predictive models attract many researchers. To bridge that gap, in this paper we compare four ensembles adapted to instance selection: Bagging, Feature Bagging, AdaBoost and Additive Noise. The last one is introduced for the first time in this paper. The study is based on empirical comparison performed on 43 datasets and 9 base instance selection methods. The experiments are divided into three scenarios. In the first one, evaluated on a single dataset, we demonstrate the influence of the ensembles on the compression–accuracy relation, in the second scenario the goal is to achieve the highest prediction accuracy, and in the third one both accuracy and the level of dataset compression constitute a multi-objective criterion. The obtained results indicate that ensembles of instance selection improve the base instance selection algorithms except for unstable methods such as CNN and IB3, which is achieved at the expense of compression. In the comparison, Bagging and AdaBoost lead in most of the scenarios. In the experiments we evaluate three classifiers: 1NN, kNN and SVM. We also note a deterioration in prediction accuracy for robust classifiers (kNN and SVM) trained on data filtered by any instance selection methods (including the ensembles) when compared with the results obtained when the entire training set was used to train these classifiers.

2

Instance selection techniques in reduction of data streams derived from medical devices

Byczkowska-Lipińska L., Wosiak A.

Przegląd Elektrotechniczny

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2017

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R. 93, nr 12

115--118

EN

The research described in this paper concerns the reduction of streams of data derived from medical devices, i.e. ECG recordings. Experimental studies included three instance selection techniques: thresholding method, bounds checking and frequent data reduction . It was shown that application the instance selection techniques may reduce data stream by over 90% without losing anomalies or the measurements that are key values for the medical diagnosis.

PL

W ramach niniejszej pracy przeprowadzona została redukcja strumienia danych pozyskanych z urządzeń medycznych. Badania eksperymentalne obejmowały zastosowanie trzech technik selekcji przypadków: metody eliminacji progowej, weryfikacji zakresu oraz redukcji obiektów częstych. W pracy zostało wykazane, że zastosowanie selekcji przypadków pozwala na redukcję strumienia danych o ponad 90% bez utraty wartości kluczowych dla postawienia diagnozy medycznej.

3

Data Selection for Neural Networks

Kordos M.

Schedae Informaticae

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2016

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Vol. 25

153--164

EN

Several approaches to joined feature and instance selection in neural network leaning are discussed and experimentally evaluated in respect to classification accuracy and dataset compression, considering also their computational complexity. These include various versions of feature and instance selection prior to the network learning, the selection embedded in the neural network and hybrid approaches, including solutions developed by us. The advantages and disadvantages of each approach are discussed and some possible improvements are proposed.

4

Self-configuring hybrid evolutionary algorithm for fuzzy imbalanced classification with adaptive instance selection

Stanovov V., Semenkin E., Semenkina O.

Journal of Artificial Intelligence and Soft Computing Research

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2016

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Vol. 6, No. 3

173--188

EN

A novel approach for instance selection in classification problems is presented. This adaptive instance selection is designed to simultaneously decrease the amount of computation resources required and increase the classification quality achieved. The approach generates new training samples during the evolutionary process and changes the training set for the algorithm. The instance selection is guided by means of changing probabilities, so that the algorithm concentrates on problematic examples which are difficult to classify. The hybrid fuzzy classification algorithm with a self-configuration procedure is used as a problem solver. The classification quality is tested upon 9 problem data sets from the KEEL repository. A special balancing strategy is used in the instance selection approach to improve the classification quality on imbalanced datasets. The results prove the usefulness of the proposed approach as compared with other classification methods.

5

Multiple-instance learning with pairwise instance similarity

Yuan L., Liu J., Tang X.

International Journal of Applied Mathematics and Computer Science

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2014

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Vol. 24, no. 3

567--577

EN

Multiple-Instance Learning (MIL) has attracted much attention of the machine learning community in recent years and many real-world applications have been successfully formulated as MIL problems. Over the past few years, several Instance Selection-based MIL (ISMIL) algorithms have been presented by using the concept of the embedding space. Although they delivered very promising performance, they often require long computation times for instance selection, leading to a low efficiency of the whole learning process. In this paper, we propose a simple and efficient ISMIL algorithm based on the similarity of pairwise instances within a bag. The basic idea is selecting from every training bag a pair of the most similar instances as instance prototypes and then mapping training bags into the embedding space that is constructed from all the instance prototypes. Thus, the MIL problem can be solved with the standard supervised learning techniques, such as support vector machines. Experiments show that the proposed algorithm is more efficient than its competitors and highly comparable with them in terms of classification accuracy. Moreover, the testing of noise sensitivity demonstrates that our MIL algorithm is very robust to labeling noise.