Fuzzy transforms method and attribute dependency in data analysis

Martino, Ferdinando Di; Loia, Vincenzo; Sessa, Salvatore

doi:10.1016/j.ins.2009.10.012

Cited by 65 publications

(43 citation statements)

References 14 publications

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“…After de-trending the data by subtracting the trend from the time series dataset, we find a fuzzy partition of the dataset into seasonal subsets to which we apply the F-transforms by checking that the chosen partition is optimal for the density of the training data. In [29,32], the authors have developed this process: in particular, four forecasting indexes are proposed for assessing the quality of the results: Mean Absolute Deviation (MAD), Mean Absolute Percentage Error (MAPE), Root Mean Square Error (RMSE) and Mean Absolute Deviation Mean (MADMEAN). An optimal fuzzy partition is found by calculating the corresponding RMSE and MADMEAN: if at least one of the two indices does not exceed a respective threshold, then the process stops and the given fuzzy partition is considered optimal.…”

Section: Tssf Methods For Forecasting Analysismentioning

confidence: 99%

“…, x n ), which approximates the data. Similar to in [32], we create a partition of n i fuzzy sets for each domain [x…”

Section: F-transform Forecasting Methodsmentioning

confidence: 99%

“…F-transforms are a powerful flexible method to be applied in various domains such as image compression [31], detection of attribute dependencies in data analysis [32] and extraction of the trend cycle of time series [33].…”

Section: Introductionmentioning

confidence: 99%

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Time Series Seasonal Analysis Based on Fuzzy Transforms

Martino

Sessa

2017

Symmetry

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Abstract:We define a new seasonal forecasting method based on fuzzy transforms. We use the best interpolating polynomial for extracting the trend of the time series and generate the inverse fuzzy transform on each seasonal subset of the universe of discourse for predicting the value of an assigned output. In the first example, we use the daily weather dataset of the municipality of Naples (Italy) starting from data collected from 2003 to 2015 making predictions on mean temperature, max temperature and min temperature, all considered daily. In the second example, we use the daily mean temperature measured at the weather station "Chiavari Caperana" in the Liguria Italian Region. We compare the results with our method, the average seasonal variation, Auto Regressive Integrated Moving Average (ARIMA) and the usual fuzzy transforms concluding that the best results are obtained under our approach in both examples. In addition, the comparison results show that, for seasonal time series that have no consistent irregular variations, the performance obtained with our method is comparable with the ones obtained using Support Vector Machine-and Artificial Neural Networks-based models.

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Section: Tssf Methods For Forecasting Analysismentioning

confidence: 99%

“…, x n ), which approximates the data. Similar to in [32], we create a partition of n i fuzzy sets for each domain [x…”

Section: F-transform Forecasting Methodsmentioning

confidence: 99%

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Time Series Seasonal Analysis Based on Fuzzy Transforms

Martino

Sessa

2017

Symmetry

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“…The F-transform compression technique [3] is a lossy compression method used in image and video analysis [4]- [18] and in data analysis [19]- [25] as well.…”

Section: Introductionmentioning

confidence: 99%

First Order Fuzzy Transform for Images Compression

Martino¹,

Sessa²,

Perfilieva³

2017

JSIP

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“…The F-transform based method [28][29][30] is used in the literature for image and video compression [29,[31][32][33], image segmentation [20], and data analysis [22,34]; indeed, in [31,32] the quality of the decoded images obtained by using the F-transform compression method is shown to be better than that one obtained with the fuzzy relation equations and fully comparable with the JPEG technique.…”

Section: Introductionmentioning

confidence: 99%

Image Matching by Using Fuzzy Transforms

Martino

Sessa

2013

Advances in Fuzzy Systems

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We apply the concept of Fuzzy Transform (for short, F-transform) for improving the results of the image matching based on the Greatest Eigen Fuzzy Set (for short, GEFS) with respect to max-min composition and the Smallest Eigen Fuzzy Set (for short, SEFS) with respect to min-max composition already studied in the literature. The direct F-transform of an image can be compared with the direct F-transform of a sample image to be matched and we use suitable indexes to measure the grade of similarity between the two images. We make our experiments on the image dataset extracted from the well-known Prima Project View Sphere Database, comparing the results obtained with this method with that one based on the GEFS and SEFS. Other experiments are performed on frames of videos extracted from the Ohio State University dataset.

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Fuzzy transforms method and attribute dependency in data analysis

Cited by 65 publications

References 14 publications

Time Series Seasonal Analysis Based on Fuzzy Transforms

Time Series Seasonal Analysis Based on Fuzzy Transforms

First Order Fuzzy Transform for Images Compression

Image Matching by Using Fuzzy Transforms

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