QR-DCA: A New Rough Data Pre-processing Approach for the Dendritic Cell Algorithm

Chelly, Zeineb; Elouedi, Zied

doi:10.1007/978-3-642-37213-1_15

Cited by 12 publications

(9 citation statements)

References 8 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…However, if we look to the DCA classifier when applied to ordered data sets, we confirm that it is capable of producing high and satisfactory classification results in comparison to the state-of-the-art mentioned classifiers. Secondly, we can notice that RC-DCA outperforms both RST-DCA and DCA O confirming the results obtained in Chelly and Elouedi (2013). This is because, first, RC-DCA is based on rough set theory of data pre-processing unlike DCA O which is based on PCA and, second, because it is based on the concept of using different features for different signals in the signal categorization step, unlike both DCA O and RST-DCA.…”

Section: Comparison With State-of-the-art Recent Methodssupporting

confidence: 81%

A Hybrid Fuzzy Maintained Classification Method Based on Dendritic Cells

Dagdia

Elouedi

2019

J Classif

Self Cite

View full text Add to dashboard Cite

The dendritic cell algorithm (DCA) is a classification algorithm based on the behavior of natural dendritic cells (DCs). In literature, DCA has given good classification results. However, DCA was known to be sensitive to the order of the instance classes. To solve this limitation, a fuzzy DCA version was developed stating that the cause of such sensitivity is related to the DCA crisp classification task (hypothesis 1). In this paper, we hypothesize that there is a second possible cause of such DCA sensitivity which is related to the possible existence of noisy instances presented in the DCA signal data set (hypothesis 2). Thus, we aim, first of all, to test the trueness of the latter hypothesis, and second, we aim to develop an overall hybrid DCA taking both hypotheses into consideration. Based on hypothesis 1, our new DCA focuses on smoothing the crisp classification task using fuzzy set theory. Based on hypothesis 2, a data set cleaning technique is used to guarantee the quality of the DCA signal data set. Results show that our proposed hybrid fuzzy maintained algorithm succeeds in obtaining results of interest.

show abstract

Section: Comparison With State-of-the-art Recent Methodssupporting

confidence: 81%

A Hybrid Fuzzy Maintained Classification Method Based on Dendritic Cells

Dagdia

Elouedi

2019

J Classif

Self Cite

View full text Add to dashboard Cite

show abstract

“…We kept the experiment environment the same as the state-of-art works [4,10]. The experiment performed on a population of one hundred cells, and ten DCs sample the antigen vector each cycle.…”

Section: Experimental Evaluationmentioning

confidence: 99%

“…The maturation threshold (Csm) is generated automatically for each DC every cycle. We also used the predefined weights matrix used for signal transformation of the DCA same as defined in the state of the art (see TABLE 1) To show the efficiency of NMF in the DCA preprocessing phase, we compared it with one of the quickest rough set algorithms: The Quickreduct (QR-DCA [10]). Our main objective of this study is to optimize the runtime of the preprocessing phase while increasing the classification rate of the DCA.…”

Section: Experimental Evaluationmentioning

confidence: 99%

“…In the first dataset, the excellent accuracy of breast10 with 98,54% translated by the high quality of factorization, where it gives a very close representation of the original data, which means that the error is insignificant. Compared with the rough set [10], the feature selection process using the rough set took more than 11.62 seconds to select relevant features with an accuracy rate of 79.80%. In comparison, NMF took only 3.06 seconds to achieve the whole process.…”

Section: Experimental Evaluationmentioning

confidence: 99%

See 1 more Smart Citation

A Corpus Based Transformation-Based Learning for Hausa Text Parts of Speech Tagging

Belhadj¹,

Cherif²,

Cheriet³

2021

IJCDS

View full text Add to dashboard Cite

“…In their work, the full reduct algorithm of rough set based is used to filter the important feature for DCA and gain better result than PCA. Later, they improved the RC-DCA model by using Quick Reduct algorithm (QR-DCA) with a higher accuracy result [6].…”

Section: Introductionmentioning

confidence: 99%

An Evaluation of Feature Selection Technique for Dendrite Cell Algorithm

Mohsin

Hamdan

Bakar

2014

2014 International Conference on IT Convergence and Security (ICITCS)

View full text Add to dashboard Cite

Dendrite cell algorithm needs appropriates feature to represents its specific input signals. Although there are many feature selection algorithms have been used in identifying appropriate features for dendrite cell signals, there are algorithms that never been investigated and limited work to compare performance among them. In this study, six feature selection algorithms namely Information Gain, Gain Ratio, Symmetrical Uncertainties, Chi Square, Support Vector Machine, and Rough Set with Genetic Algorithm Reduct are examined and their effectiveness to represent dendrite cell signal are evaluated. Eight universal datasets are chosen and assessing their performance according to sensitivity, specificity, and accuracy. From the experiment, the Rough Set Genetic Algorithm reduct is found to be the most effect feature selection for dendrite cell algorithm when it generates a consistent result for all evaluation metrics. In single evaluation metrics, the chi square technique has the best competence in term of sensitiveness while the rough set genetic algorithm reduct is good at specificity and accuracy. In the next step, further analysis will be conducted on complex dataset such as time series data set.

show abstract

QR-DCA: A New Rough Data Pre-processing Approach for the Dendritic Cell Algorithm

Cited by 12 publications

References 8 publications

A Hybrid Fuzzy Maintained Classification Method Based on Dendritic Cells

A Hybrid Fuzzy Maintained Classification Method Based on Dendritic Cells

A Corpus Based Transformation-Based Learning for Hausa Text Parts of Speech Tagging

An Evaluation of Feature Selection Technique for Dendrite Cell Algorithm

Contact Info

Product

Resources

About