Ensembles of Bireducts: Towards Robust Classification and Simple Representation

Ślęzak, Dominik; Janusz, Andrzej

doi:10.1007/978-3-642-27142-7_9

Cited by 32 publications

(39 citation statements)

References 13 publications

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“…To tackle the above challenges, a new extension of the original notion of a reduct was proposed [29,36], called a decision bireduct. In this approach the emphasis is on both, a subset of attributes that describes the decision classes and a subset of objects for which such a description is possible.…”

Section: Ddr Is a Decision Reduct Of Sufficiently Manymentioning

confidence: 99%

Algorithms for Similarity Relation Learning from High Dimensional Data

Janusz

2014

Transactions on Rough Sets XVII

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Abstract. The notion of similarity plays an important role in machine learning and artificial intelligence. It is widely used in tasks related to a supervised classification, clustering, an outlier detection and planning. Moreover, in domains such as information retrieval or case-based reasoning, the concept of similarity is essential as it is used at every phase of the reasoning cycle. The similarity itself, however, is a very complex concept that slips out from formal definitions. A similarity of two objects can be different depending on a considered context. In many practical situations it is difficult even to evaluate the quality of similarity assessments without considering the task for which they were performed. Due to this fact the similarity should be learnt from data, specifically for the task at hand. This paper presents a research on the problem of similarity learning, which is a part of author's PHD dissertation. It describes a similarity model, called Rule-Based Similarity, and shows algorithms for constructing this model from available data. The model utilizes notions from the rough set theory to derive a similarity function that allows to approximate the similarity relation in a given context. It is largely inspired by the idea of Tversky's feature contrast model and it has several analogical properties. In the paper, those theoretical properties are described and discussed. Moreover, the paper presents results of experiments on real-life data sets, in which a quality of the proposed model is thoroughly evaluated and compared with the state-of-the-art algorithms.

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Section: Ddr Is a Decision Reduct Of Sufficiently Manymentioning

confidence: 99%

Algorithms for Similarity Relation Learning from High Dimensional Data

Janusz

2014

Transactions on Rough Sets XVII

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“…The motivation to introduce decision bireducts was to allow for explicit analysis whether classifiers that use different selected subsets of attributes do not repeat classification mistakes on the same objects in the training data set. Experiments reported in [9] show that diversification of subsets of attributes with this respect may be important in practice. The original ordering algorithm to calculate a decision bireduct for a given data set is presented in Algorithm 5.…”

Section: Bireducts and Generalized Majority Decisionmentioning

confidence: 99%

“…The first formulation of decision bireducts occurred in [9], [6], where their Boolean characterization was described and a simple ordering algorithm aimed at their heuristic extraction from data was proposed by an analogy to classical decision reducts. The motivation to introduce decision bireducts was to allow for explicit analysis whether classifiers that use different selected subsets of attributes do not repeat classification mistakes on the same objects in the training data set.…”

Section: Bireducts and Generalized Majority Decisionmentioning

confidence: 99%

“…In our previous work [6] we compared approximate decision reducts based on feature subset quality functions [7], [8] with decision bireducts [9] -another extension to the rough set framework of decision reducts. In short, a decision bireduct is an irreducible feature subset which preserves the information about the decision but only on selected objects from the data set.…”

Section: Introductionmentioning

confidence: 99%

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Generalized Majority Decision Reducts

Widz

Stawicki

2016

Annals of Computer Science and Information Systems

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Abstract-We discuss several new methods for constructing approximate decision reducts from the rough set theory. We introduce generalized approximate majority decision reducts, which are an extension to standard approximate decision reducts known from literature but with improved calculation performance and complexity. We also discuss the relationship and differences of the new approximate decision reduct notion with so called decision bireducts -another type of approximate decision reducts.

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“…Because of the pre-processing of data that has been provided in the competition, the real problem of monitoring the firefighters activities, which is originally associated with processing of streams of sensor readings [10] that constituting time series [2], has been reduced for the problem of classification [25], more precisely to multi-labeling [26]. Original sensor readings has been pre-processed and subdivided into frames [21], [28] of given length and made available in a csv file.…”

Section: Classifier Training and Labelingmentioning

confidence: 99%

Window-Based Feature Extraction Framework for Multi-Sensor Data: A Posture Recognition Case Study

Grzegorowski¹,

Stawicki²

2015

Annals of Computer Science and Information Systems

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Abstract-The article introduces a novel mechanism for automatic extraction of features from streams of numerical data. It was originally designed for the purpose of processing multiple streams of readings generated by sensors in coal mines. The original research was conducted on methane concentration analysis in the DISESOR project. The article demonstrates an application of the elaborated mechanism for the case of tagging short series of readings from sensors that monitor activities and movements of firefighters during the action with labels corresponding to firefighter activities. The purpose of the experiment was to assess how the automatic feature extraction and construction of classifiers (without parameters tuning and without the use of classifier ensembles) can cope with the competition's task in comparison to other participants.

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Ensembles of Bireducts: Towards Robust Classification and Simple Representation

Cited by 32 publications

References 13 publications

Algorithms for Similarity Relation Learning from High Dimensional Data

Algorithms for Similarity Relation Learning from High Dimensional Data

Generalized Majority Decision Reducts

Window-Based Feature Extraction Framework for Multi-Sensor Data: A Posture Recognition Case Study

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