Integration of Linear SVM Classifiers in Geometric Space Using the Median

Burduk, Robert; Biedrzycki, Jedrzej

doi:10.1007/978-3-030-03658-4_4

Cited by 3 publications

(3 citation statements)

References 17 publications

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“…Based on operations in geometrical space generated by real-valued features this procedure has proven itself to be effective in comparison to others, commonly used integration techniques such as majority voting [7]. In one of the previous papers authors have shown significant improvement in the classification by applying weighted mean and median functional to decision boundaries of the SVM classifiers [8].…”

Section: Related Workmentioning

confidence: 99%

Weighted Scoring in Geometric Space for Decision Tree Ensemble

Biedrzycki¹,

Burduk²

2020

IEEE Access

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In order to improve the classification performance of a single classification model, Multiple Classifier Systems (MCS) are used. One of the most common techniques utilizing multiple decision trees is the random forest, where diversity between base classifiers is obtained by bagging the training dataset. In this paper, we propose the algorithm that uses horizontal partitioning the learning set and uses decision trees as base models to obtain decision regions. In the proposed approach feature space is divided into disjoint subspace. Additionally, the location of the subspace centroids, as well as the size and location of decision regions, are used in order to determine the weights needed in the last process of creating MCS, i.e. in the integration phase. The proposed algorithm was evaluated employing multiple open-source benchmarking datasets, compared using accuracy and Matthews correlation coefficient performance measures with two existing MCS methods-random forest and majority voting. The statistical analysis confirms an improvement in recognition compared to the random forest. In addition, we proved that for infinitely dense space division proposed algorithm is equivalent to majority voting. INDEX TERMS Decision tree, ensemble classifier, majority voting, multiple classifier system, random forest.

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Section: Related Workmentioning

confidence: 99%

Weighted Scoring in Geometric Space for Decision Tree Ensemble

Biedrzycki¹,

Burduk²

2020

IEEE Access

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“…Based on transformations in the geometrical space spread on real-valued, non-categorical features this procedure has proven itself to be more effective in comparison to others, commonly used integration techniques such as majority voting [ 21 ]. The authors have studied and proved the effectiveness of an integration algorithm based on averaging and taking median of values of the decision boundary in the SVM classifiers [ 22 ]. Next, two algorithms for decision trees were proposed and evaluated [ 23 , 24 ].…”

Section: Related Workmentioning

confidence: 99%

Decision Tree Integration Using Dynamic Regions of Competence

Biedrzycki

Burduk

2020

Entropy

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A vital aspect of the Multiple Classifier Systems construction process is the base model integration. For example, the Random Forest approach used the majority voting rule to fuse the base classifiers obtained by bagging the training dataset. In this paper we propose the algorithm that uses partitioning the feature space whose split is determined by the decision rules of each decision tree node which is the base classification model. After dividing the feature space, the centroid of each new subspace is determined. This centroids are used in order to determine the weights needed in the integration phase based on the weighted majority voting rule. The proposal was compared with other Multiple Classifier Systems approaches. The experiments regarding multiple open-source benchmarking datasets demonstrate the effectiveness of our method. To discuss the results of our experiments, we use micro and macro-average classification performance measures.

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“…A common approach to deal with dispersed data is to build a separate local model based on each local table and then combine the local prediction results [ 5 , 6 , 7 ]. In the stage of combining the prediction results, we can use fusion methods [ 8 ] from three different levels (measurement level, rank level and abstract level).…”

Section: Introductionmentioning

confidence: 99%

Neural Network Used for the Fusion of Predictions Obtained by the K-Nearest Neighbors Algorithm Based on Independent Data Sources

Przybyła–Kasperek

Marfo

2021

Entropy

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The article concerns the problem of classification based on independent data sets—local decision tables. The aim of the paper is to propose a classification model for dispersed data using a modified k-nearest neighbors algorithm and a neural network. A neural network, more specifically a multilayer perceptron, is used to combine the prediction results obtained based on local tables. Prediction results are stored in the measurement level and generated using a modified k-nearest neighbors algorithm. The task of neural networks is to combine these results and provide a common prediction. In the article various structures of neural networks (different number of neurons in the hidden layer) are studied and the results are compared with the results generated by other fusion methods, such as the majority voting, the Borda count method, the sum rule, the method that is based on decision templates and the method that is based on theory of evidence. Based on the obtained results, it was found that the neural network always generates unambiguous decisions, which is a great advantage as most of the other fusion methods generate ties. Moreover, if only unambiguous results were considered, the use of a neural network gives much better results than other fusion methods. If we allow ambiguity, some fusion methods are slightly better, but it is the result of this fact that it is possible to generate few decisions for the test object.

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Integration of Linear SVM Classifiers in Geometric Space Using the Median

Cited by 3 publications

References 17 publications

Weighted Scoring in Geometric Space for Decision Tree Ensemble

Weighted Scoring in Geometric Space for Decision Tree Ensemble

Decision Tree Integration Using Dynamic Regions of Competence

Neural Network Used for the Fusion of Predictions Obtained by the K-Nearest Neighbors Algorithm Based on Independent Data Sources

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