STAC: A web platform for the comparison of algorithms using statistical tests

Rodríguez-Fdez, Ismael; Canosa, Adrian; Mucientes, Manuel; Bugarín, Alberto

doi:10.1109/fuzz-ieee.2015.7337889

Cited by 165 publications

(79 citation statements)

References 15 publications

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“…In order to analyze the statistical significance of these results the STAC platform [26] was used to apply the statistical tests. A Friedman test was used for both the number of rules and the test error in order to get a ranking of the algorithms and check whether the differences between them were statistically significant.…”

Section: Discussionmentioning

confidence: 99%

FRULER: Fuzzy Rule Learning through Evolution for Regression

Rodríguez-Fdez

Mucientes

Bugarín

2016

Information Sciences

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In regression problems, the use of TSK fuzzy systems is widely extended due to the precision of the obtained models. Moreover, the use of simple linear TSK models is a good choice in many real problems due to the easy understanding of the relationship between the output and input variables. In this paper we present FRULER, a new genetic fuzzy system for automatically learning accurate and simple linguistic TSK fuzzy rule bases for regression problems. In order to reduce the complexity of the learned models while keeping a high accuracy, the algorithm consists of three stages: instance selection, multi-granularity fuzzy discretization of the input variables, and the evolutionary learning of the rule base that uses the Elastic Net regularization to obtain the consequents of the rules. Each stage was validated using 28 real-world datasets and FRULER was compared with three state of the art genetic fuzzy systems. Experimental results show that FRULER achieves the most accurate and simple models compared even with approximative approaches.

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Section: Discussionmentioning

confidence: 99%

FRULER: Fuzzy Rule Learning through Evolution for Regression

Rodríguez-Fdez

Mucientes

Bugarín

2016

Information Sciences

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“…For the test, we set the null hypothesis (H 0 ) as The following tests are applied to the STAC web platform where it is assumed that the lower the result of an algorithm on a problem, the better of such algorithm [29]. The null hypothesis for Friedman rank test is rejected at both the testbeds with a p value much lower than the significance level.…”

Section: Statistical Validation Of the Presented Resultmentioning

confidence: 99%

Practical Fingerprinting Localization for Indoor Positioning System by Using Beacons

Subedi

Pyun

2017

Journal of Sensors

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Recent developments in the fields of smartphones and wireless communication technologies such as beacons, Wi-Fi, and ultrawideband have made it possible to realize indoor positioning system (IPS) with a few meters of accuracy. In this paper, an improvement over traditional fingerprinting localization is proposed by combining it with weighted centroid localization (WCL). The proposed localization method reduces the total number of fingerprint reference points over the localization space, thus minimizing both the time required for reading radio frequency signals and the number of reference points needed during the fingerprinting learning process, which eventually makes the process less time-consuming. The proposed positioning has two major steps of operation. In the first step, we have realized fingerprinting that utilizes lightly populated reference points (RPs) and WCL individually. Using the location estimated at the first step, WCL is run again for the final location estimation. The proposed localization technique reduces the number of required fingerprint RPs by more than 40% compared to normal fingerprinting localization method with a similar localization estimation error.

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“…In order to test whether these differences are statistically significant or not, we conducted a nonparametric test using the binomial sign test for two dependent samples with a total of 20 datasets (5 WSC w/response time + 5 WSC w/throughput + 5 Random w/response time + 5 Random w/throughput). The null hypothesis was rejected with p-value ≈ 0.01 [41], meaning that both strategies (local and global search) find significantly different solutions. Thus, a hybrid strategy can perform better in many different scenarios, since it achieves a good tradeoff between quality and execution time.…”

Section: Randomly Generated Datasetsmentioning

confidence: 98%

Hybrid Optimization Algorithm for Large-Scale QoS-Aware Service Composition

Rodríguez-Mier

Mucientes

Lama

2017

IEEE Trans. Serv. Comput.

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In this paper we present a hybrid approach for automatic composition of Web services that generates semantic inputoutput based compositions with optimal end-to-end QoS, minimizing the number of services of the resulting composition. The proposed approach has four main steps: 1) generation of the composition graph for a request; 2) computation of the optimal composition that minimizes a single objective QoS function; 3) multi-step optimizations to reduce the search space by identifying equivalent and dominated services; and 4) hybrid local-global search to extract the optimal QoS with the minimum number of services. An extensive validation with the datasets of the Web Service Challenge 2009-2010 and randomly generated datasets shows that: 1) the combination of local and global optimization is a general and powerful technique to extract optimal compositions in diverse scenarios; and 2) the hybrid strategy performs better than the state-of-the-art, obtaining solutions with less services and optimal QoS.

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STAC: A web platform for the comparison of algorithms using statistical tests

Cited by 165 publications

References 15 publications

FRULER: Fuzzy Rule Learning through Evolution for Regression

FRULER: Fuzzy Rule Learning through Evolution for Regression

Practical Fingerprinting Localization for Indoor Positioning System by Using Beacons

Hybrid Optimization Algorithm for Large-Scale QoS-Aware Service Composition

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