Statistical Comparisons of the Top 10 Algorithms in Data Mining for Classi cation Task

Settouti, Nesma; Bechar, Mohammed El Amine; Chikh, Mohammed Amine

doi:10.9781/ijimai.2016.419

Cited by 61 publications

(34 citation statements)

References 7 publications

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“…Presented results support the neural networks capacity to solve statistical [39] and parameter estimation [40] problems. The current proposal applied a novel feature extraction method by using histograms of groups of K samples, which was combined with deep learning and classifier fusion approaches to produce an improved K parameter estimator.…”

Section: B Significance Of Resultsmentioning

confidence: 73%

Improved Shape Parameter Estimation in K Clutter with Neural Networks and Deep Learning

Fernández¹,

Vidal²

2016

IJIMAI

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-The discrimination of the clutter interfering signal is a current problem in modern radars' design, especially in coastal or offshore environments where the histogram of the background signal often displays heavy tails. The statistical characterization of this signal is very important for the cancellation of sea clutter, whose behavior obeys a K distribution according to the commonly accepted criterion. By using neural networks, the authors propose a new method for estimating the K shape parameter, demonstrating its superiority over the classic alternative based on the Method of Moments. Whereas both solutions have a similar performance when the entire range of possible values of the shape parameter is evaluated, the neuronal alternative achieves a much more accurate estimation for the lower Fig.s of the parameter. This is exactly the desired behavior because the best estimate occurs for the most aggressive states of sea clutter. The final design, reached by processing three different sets of computer generated K samples, used a total of nine neural networks whose contribution is synthesized in the final estimate, thus the solution can be interpreted as a deep learning approximation. The results are to be applied in the improvement of radar detectors, particularly for maintaining the operational false alarm probability close to the one conceived in the design.

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Section: B Significance Of Resultsmentioning

confidence: 73%

Improved Shape Parameter Estimation in K Clutter with Neural Networks and Deep Learning

Fernández¹,

Vidal²

2016

IJIMAI

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“…Our method, however, uses many different information sources about the individual cases without assuming any relationship between these factors and transmission. Additionally, our method uses naive Bayes, a simple but powerful machine learning tool that has many diverse applications (33,(49)(50)(51)(52). Although traditionally a naive Bayes model is trained with a set of true events, our method performs almost as well when SNP distance is used as a transmission proxy.…”

Section: Discussionmentioning

confidence: 99%

Estimating the Relative Probability of Direct Transmission between Infectious Disease Patients

Leavitt

Lee

Sebastiani

et al. 2019

Preprint

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BackgroundEstimating infectious disease parameters such as the serial interval (time between symptom onset in primary and secondary cases) and reproductive number (average number of secondary cases produced by a primary case) are important to understand infectious disease dynamics. Many estimation methods require linking cases by direct transmission, a difficult task for most diseases. MethodsUsing a subset of cases with detailed genetic or contact investigation data to develop a training set of probable transmission events, we build a model to estimate the relative transmission probability for all case-pairs from demographic, spatial and clinical data. Our method is based on naive Bayes, a machine learning classification algorithm which uses the observed frequencies in the training dataset to estimate the probability that a pair is linked given a set of covariates. ResultsIn simulations we find that the probabilities estimated using genetic distance between cases to define training transmission events are able to distinguish between truly linked and unlinked pairs with high accuracy (area under the receiver operating curve value of 95%). Additionally only a subset of the cases, 10-50% depending on sample size, need to have detailed genetic data for our method to perform well. We show how these probabilities can be used to estimate the average effective reproductive number and apply our method to a tuberculosis outbreak in Hamburg, Germany. ConclusionsAll rights reserved. No reuse allowed without permission.The copyright holder for this preprint (which was not peer-reviewed) is the author/funder. . https://doi.org/10.1101/612945 doi: bioRxiv preprint 3 Our method is a novel way to infer transmission dynamics in any dataset when only a subset of cases has rich contact investigation and/or genetic data.

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“…K-means is one of the well-known algorithms for clustering [28], [29] of which various modifications have been proposed including fuzzy logic [30]. In this algorithm each cluster is characterized by its center point i.e.…”

Section: A Integrated Scheme Of Customer Segmentation With Campaign mentioning

confidence: 99%

A Fuzzy Linguistic RFM Model Applied to Campaign Management

Carrasco¹,

López²,

García‐Madariaga³

et al. 2019

IJIMAI

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In the literature there are some proposals for integrated schemes for campaign management based on segmentation from the results of the RFM model. RFM is a technique used to analyze customer behavior by means of three variables: Recency, Frequency and Monetary value. It is s very much in use in the business world due to its simplicity of use, implementation and interpretability of its results. However, RFM applications to campaign management present known limitations like the lack of precision because the scores of these variables are expressed by an ordinal scale. In this paper, we propose to link customer segmentation methods with campaign activities in a more effective way incorporating the 2-tuple model both to the RFM calculation process and to its subsequent exploitation by means of segmentation algorithms, specifically, k-means. This yields a greater interpretability of these results and also allows computing these values without loss of information. Therefore, marketers can effectively develop more effective marketing strategy.

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Statistical Comparisons of the Top 10 Algorithms in Data Mining for Classi cation Task

Cited by 61 publications

References 7 publications

Improved Shape Parameter Estimation in K Clutter with Neural Networks and Deep Learning

Improved Shape Parameter Estimation in K Clutter with Neural Networks and Deep Learning

Estimating the Relative Probability of Direct Transmission between Infectious Disease Patients

A Fuzzy Linguistic RFM Model Applied to Campaign Management

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