Fuzziness based sample categorization for classifier performance improvement

Wang, Xizhao; Ashfaq, Rana Aamir Raza; Fu, Aimin

doi:10.3233/ifs-151729

Cited by 143 publications

(25 citation statements)

References 35 publications

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“…Data clustering is a fundamental tool for data analysis that aims to identify some inherent structure present in a set of objects. Based on this conclusion, clustering has been proved to be a key tool in machine learning and data mining [1][2][3][4][5][6][7][8][9].…”

Section: Introductionmentioning

confidence: 99%

Incremental enhanced α-expansion move for large data: a probability regularization perspective

Wang

2016

Int. J. Mach. Learn. & Cyber.

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To deal with large data clustering tasks, an incremental version of exemplar-based clustering algorithm is proposed in this paper. The novel clustering algorithm, called Incremental Enhanced a-Expansion Move (IEEM), processes large data chunk by chunk. The work here includes two aspects. First, in terms of the maximum a posteriori principle, a unified target function is developed to unify two typical exemplar-based clustering algorithms, namely Affinity Propagation (AP) and Enhanced aExpansion Move (EEM). Secondly, with the proposed target function, the probability based regularization term is proposed and accordingly the proposed target function is extended to make IEEM have the ability to improve clustering performance of the entire dataset by leveraging the clustering result of previous chunks. Another outstanding characteristic of IEEM is that only by modifying the definitions of several variables used in EEM, the minimization procedure of EEM and its theoretical spirit can be easily kept in IEEM, and hence no more efforts are needed to develop a new optimization algorithm for IEEM. In contrast to AP, EEM and the existing incremental clustering algorithm IMMFC, our experimental results of synthetic and real-world datasets indicate the effectiveness of IEEM.

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Section: Introductionmentioning

confidence: 99%

Incremental enhanced α-expansion move for large data: a probability regularization perspective

Wang

2016

Int. J. Mach. Learn. & Cyber.

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“…Besides, learning cognitive concepts from fuzzy data [31][32][33] also deserves to be investigated. Undoubtedly, approaches to cognitive concept learning from incomplete information cannot be directly extended to the case of fuzzy information since both knowledge representation and information measure are extremely different [1,9,30]. These issues will be studied in our future work.…”

Section: Final Remarksmentioning

confidence: 95%

Cognitive concept learning from incomplete information

Zhao

Liu

et al. 2016

Int. J. Mach. Learn. & Cyber.

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Cognitive concept learning is to learn concepts from a given clue by simulating human thought processes including perception, attention and thinking. In recent years, it has attracted much attention from the communities of formal concept analysis, cognitive computing and granular computing. However, the classical cognitive concept learning approaches are not suitable for incomplete information. Motivated by this problem, this study mainly focuses on cognitive concept learning from incomplete information. Specifically, we put forward a pair of approximate cognitive operators to derive concepts from incomplete information. Then, we propose an approximate cognitive computing system to perform the transformation between granular concepts as incomplete information is updated periodically. Moreover, cognitive processes are simulated based on three types of similarities. Finally, numerical experiments are conducted to evaluate the proposed cognitive concept learning methods.

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“…Probability uncertainty and fuzziness uncertainty processing play a key role in boost-ing classification systems including extreme learning machines and decision trees (Wang et al, 2015;Lu et al, 2015). PNN is essentially a classifier that places the Bayes estimate in a feed-forward neural network.…”

Section: Outline Of Pnnmentioning

confidence: 99%

PNN-based Rockburst Prediction Model and Its Applications

Zhou

Wang

2017

Earth sci. res. j.

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Rock burst is one of main engineering geological problems greatly threatening the safety of construction. Prediction of rock burst is always an important issue concerning the safety of workers and equipments in tunnels. In this paper, a novel PNN-based rock burst prediction model is proposed to determine whether rock burst will happen in the underground rock projects and how much the intensity of rock burst is. The probabilistic neural network (PNN) is developed based on Bayesian criteria of multivariate pattern classification. Because PNN has the advantages of low training complexity, high stability, quick convergence, and simple construction, it can be well applied in the prediction of rock burst. Some main control factors, such as rocks' maximum tangential stress , rocks' uniaxial compressive strength , rocks' uniaxial tensile strength and elastic energy index of rock are chosen as the characteristic vector of PNN. PNN model is obtained through training data sets of rock burst samples which come from underground rock project in domestic and abroad. Other samples are tested with the model. The testing results agree with the practical records. At the same time, two realworld applications are used to verify the proposed method. The results of prediction are same as the results of existing methods, just same as what happened in the scene, which verifies the effectiveness and applicability of our proposed work. El fracturamiento o explosión de rocas es uno de los principales problemas en ingeniería geológica que amenaza significativamente la seguridad de una construcción. La predicción del fracturamiento de rocas es importante para la seguridad de los trabajadores y el equipamiento en túneles. En este artículo se propone un nuevo modelo de predicción de fracturamiento de rocas basado en una red neuronal probabilística (PNN por sus siglas en inglés) para determinar la posible ocurrencia e intensidad de uno de estos eventos en proyectos subterráneos. La PNN se desarrolló con base en un criterio Bayesiano para la clasificación multivariada de patrones. Debido a que la PNN tiene las ventajas de una menor complejidad de adiestramiento, estabilidad, rápida convergencia y simplicidad en su construcción, se puede adecuar en la predicción del fracturamiento de rocas. Algunos factores principales de control, como la fuerza máxima tangencial de rocas, la resistencia de compresión uniaxial, la fuerza de tensión uniaxial, y el índice de energía elástica de las rocas fueron escogidos como los vectores característicos de la PNN. El modelo se obtuvo a través del adiestramiento de datos sobre fracturamiento de rocas en proyectos subterráneos en diferentes localidades. Otras datos también se analizaron con el modelo. Los resultados de la evaluación se ajustan a los registros observados. Simultáneamente, se utilizaron dos aplicaciones prácticas para verificar el método propuesto. Los resultados de la predicción son similares a los de métodos existentes, un factor que además se presentó en las pruebas de campo, lo que demuestra la ef...

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Fuzziness based sample categorization for classifier performance improvement

Cited by 143 publications

References 35 publications

Incremental enhanced α-expansion move for large data: a probability regularization perspective

Incremental enhanced α-expansion move for large data: a probability regularization perspective

Cognitive concept learning from incomplete information

PNN-based Rockburst Prediction Model and Its Applications

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