A new fuzzy-rough feature selection algorithm for mammographic risk analysis

Guo, Qian; Qu, Yanpeng; Deng, Ansheng; Yang, Longzhi

doi:10.1109/fskd.2016.7603303

Cited by 6 publications

(4 citation statements)

References 23 publications

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“…The inspiration from the DT and the behaviour of DCs has led to the development of the DCA which is a population based binary classification system. Firstly, feature selection process is applied on the training data to select the most informative features, and many feature selection approaches have been proposed in the literature which can be readily used here, such as [13], [14] This is followed by four phases of the DCA, including signal categorisation, context detection, context assignment and labeling as introduced below.…”

Section: Dendritic Cell Algorithm (Dca)mentioning

confidence: 99%

A Comparative Study of Genetic Algorithm and Particle Swarm optimisation for Dendritic Cell Algorithm

Elisa

Yang

Chao

et al. 2020

2020 IEEE Congress on Evolutionary Computation (CEC)

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Dendritic cell algorithm (DCA) is a class of artificial immune systems that was originally developed for anomaly detection in networked systems and later as a general binary classifier. Conventionally, in its life cycle, the DCA goes through four phases including feature categorisation into artificial signals, context detection of data items, context assignment, and finally labeling of data items as either abnormal or normal class. During the context detection phase, the DCA requires users to manually pre-define the parameters used by its weighted function to process the signals and data items. Notice that the manual derivation of the parameters of the DCA cannot guarantee the optimal set of weights being used, research attention has thus been attracted to the optimisation of the parameters. This paper reports a systematic comparative study between Genetic algorithm (GA) and Particle Swarm Optimisation (PSO) on parameter optimisation for DCA. In order to evaluate the performance of GA-DCA and PSO-DCA, twelve publicly available datasets from UCI machine learning repository were employed. The performance results based on the computational time, classification accuracy, sensitivity, F-measure, and precision show that, the GA-DCA overall outperforms PSO-DCA for most of the datasets.

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Section: Dendritic Cell Algorithm (Dca)mentioning

confidence: 99%

A Comparative Study of Genetic Algorithm and Particle Swarm optimisation for Dendritic Cell Algorithm

Elisa

Yang

Chao

et al. 2020

2020 IEEE Congress on Evolutionary Computation (CEC)

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“…The extracted feature dimension was fixed to 80. This was followed by selecting 50 attributes only from the extracted 80 attributes using the fuzzy-rough feature selection (FRFS) approach ( [26]). For normalising the selected features, as evaluated by [2], existing techniques, such as minmax (MM) normalisation, 1 -normalisation, 2 -normalisation, power normalisation (PN), and their variants (i.e.…”

Section: B Experimental Setupmentioning

confidence: 99%

Adaptive Activation Function Generation for Artificial Neural Networks through Fuzzy Inference with Application in Grooming Text Categorisation

Zuo

Wei

et al. 2019

2019 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)

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The activation function is introduced to determine the output of neural networks by mapping the resulting values of neurons into a specific range. The activation functions often suffer from 'gradient vanishing', 'non zero-centred function outputs', 'exploding gradients', and 'dead neurons', which may lead to deterioration in the classification performance. This paper proposes an activation function generation approach using the Takagi-Sugeno-Kang inference in an effort to address such challenges. In addition, the proposed method further optimises the coefficients in the activation function using the genetic algorithm such that the activation function can adapt to different applications. This approach has been applied to a digital forensics application of online grooming detection. The evaluations confirm the superiority of the proposed activation function for online grooming detection using an imbalanced data set.

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“…The fuzzyrough feature selection algorithm is shown in Algorithm 1. Fuzzy-rough feature selection method can be extended to adapt to a wider range of real-world data mining and knowledge discovery problems such as mammographic risk analysis [17], [18]. However, it has not been applied for solving online child grooming detection problem.…”

Section: B Fuzzy Rough Feature Selectionmentioning

confidence: 99%

Grooming Detection using Fuzzy-Rough Feature Selection and Text Classification

Zuo

Anderson

et al. 2018

2018 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)

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Online child grooming detection has recently attracted intensive research interests from both the machine learning community and digital forensics community due to its great social impact. The existing data-driven approaches usually face the challenges of lack of training data and the uncertainty of classes in terms of the classification or decision boundary. This paper proposes a grooming detection approach in an effort to address such uncertainty based on a data set derived from a publicly available profiling data set. In particular, the approach firstly applies the conventional text feature extraction approach in identifying the most significant words in the data set. This is followed by the application of a fuzzy-rough feature selection approach in reducing the high dimensions of the selected words for fast processing, which at the same time addressing the uncertainty of class boundaries. The experimental results demonstrate the efficiency and efficacy of the proposed approach in detecting child grooming.

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A new fuzzy-rough feature selection algorithm for mammographic risk analysis

Cited by 6 publications

References 23 publications

A Comparative Study of Genetic Algorithm and Particle Swarm optimisation for Dendritic Cell Algorithm

A Comparative Study of Genetic Algorithm and Particle Swarm optimisation for Dendritic Cell Algorithm

Adaptive Activation Function Generation for Artificial Neural Networks through Fuzzy Inference with Application in Grooming Text Categorisation

Grooming Detection using Fuzzy-Rough Feature Selection and Text Classification

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