CHI-BD: A fuzzy rule-based classification system for Big Data classification problems

Elkano, Mikel; Galar, Mikel; Sanz, José Antonio; Bustince, Humberto

doi:10.1016/j.fss.2017.07.003

Cited by 71 publications

(62 citation statements)

References 29 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Considering all these must, the use of global fuzzy learning models must be analyzed 8 . This type of approaches consider a core driver procedure to compile the partial results that are obtained by successive iterations of the algorithm.…”

Section: Lessons Learned and Discussionmentioning

confidence: 99%

See 1 more Smart Citation

Why Linguistic Fuzzy Rule Based Classification Systems perform well in Big Data Applications?

Fernández¹,

Altalhi²,

Alshomrani³

et al. 2017

IJCIS

View full text Add to dashboard Cite

The significance of addressing Big Data applications is beyond all doubt. The current ability of extracting interesting knowledge from large volumes of information provides great advantages to both corporations and academia. Therefore, researchers and practitioners must deal with the problem of scalability so that Machine Learning and Data Mining algorithms can address Big Data properly. With this end, the MapReduce programming framework is by far the most widely used mechanism to implement fault-tolerant distributed applications. This novel framework implies the design of a divide-and-conquer mechanism in which local models are learned separately in one stage (Map tasks) whereas a second stage (Reduce) is devoted to aggregate all sub-models into a single solution. In this paper, we focus on the analysis of the behavior of Linguistic Fuzzy Rule Based Classification Systems when embedded into a MapReduce working procedure. By retrieving different information regarding the rules learned throughout the MapReduce process, we will be able to identify some of the capabilities of this particular paradigm that allowed them to provide a good performance when addressing Big Data problems. In summary, we will show that linguistic fuzzy classifiers are a robust approach in case of scalability requirements.

show abstract

Section: Lessons Learned and Discussionmentioning

confidence: 99%

“…We may find several works in different contexts such as classification 8 , regression 9 , and subgroup discovery 10 .…”

Section: Introductionmentioning

confidence: 99%

Why Linguistic Fuzzy Rule Based Classification Systems perform well in Big Data Applications?

Fernández¹,

Altalhi²,

Alshomrani³

et al. 2017

IJCIS

View full text Add to dashboard Cite

show abstract

“…Therefore, the result of each dataset was computed as the average of the five partitions. We considered all the open-source fuzzy classifiers available for Big Data so far 6 (CHI-BD [4], Chi-Spark-RS [6], CFM-BD [5], and FBDT/FMDT [3]) and two of the bestperforming fuzzy classifiers for Small Data (FARC-HD [11] and FURIA [2]). Although the models and learning algorithms used by FRBCSs and FDTs are different, the leaves of FDTs can be converted into a set of IF-THEN rules, allowing us to compare the accuracy-interpretability tradeoff of both types of classifiers.…”

Section: A Data and Methodsmentioning

confidence: 99%

Do we still need fuzzy classifiers for Small Data in the Era of Big Data?

Elkano

Bustince

Galar

2019

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

Self Cite

View full text Add to dashboard Cite

The Era of Big Data has forced researchers to explore new distributed solutions for building fuzzy classifiers, which often introduce approximation errors or make strong assumptions to reduce computational and memory requirements. As a result, Big Data classifiers might be expected to be inferior to those designed for standard classification tasks (Small Data) in terms of accuracy and model complexity. To our knowledge, however, there is no empirical evidence to confirm such a conjecture yet. Here, we investigate the extent to which stateof-the-art fuzzy classifiers for Big Data sacrifice performance in favor of scalability. To this end, we carry out an empirical study that compares these classifiers with some of the best performing algorithms for Small Data. Assuming the latter were generally designed for maximizing performance without considering scalability issues, the results of this study provide some intuition around the tradeoff between performance and scalability achieved by current Big Data solutions. Our findings show that, although slightly inferior, Big Data classifiers are gradually catching up with state-of-the-art classifiers for Small data, suggesting that a unified learning algorithm for Big and Small Data might be possible.

show abstract

“…Very few works can be found among the recent literature on this regard. At this respect, some works can be found expanding Chi's approach studying the effect of class imbalance (Fernández et al, ), the application of type 2 interval‐valued fuzzy logic (Sanz, Fernández, Bustince, & Herrera, ), and the generalization of the algorithm in the context of big data (Elkano, Galar, Sanz, & Bustince, ; López, del Río, Benítez, & Herrera, ).…”

Section: Interpretation Within the State‐of‐the‐artmentioning

confidence: 99%

“…This has the drawback of needing of additional parameterization and computation metrics that increase the complexity of the approach (Wang, 2003). Chi's approach studying the effect of class imbalance (Fernández et al, 2007), the application of type 2 interval-valued fuzzy logic (Sanz, Fernández, Bustince, & Herrera, 2010), and the generalization of the algorithm in the context of big data (Elkano, Galar, Sanz, & Bustince, 2017;López, del Río, Benítez, & Herrera, 2015).…”

Section: Interpretation Within the State-of-the-artmentioning

confidence: 99%

Revisiting the Wang–Mendel algorithm for fuzzy classification

Álvarez-Estévez

Moret‐Bonillo

2018

Expert Systems

View full text Add to dashboard Cite

In this paper, we review the Wang–Mendel algorithm for the induction of fuzzy IF‐THEN rules in the context of classification problems. A general fuzzy inference architecture for classification is proposed with the aim of studying the influence of alternative configurations of the learning model. Specifically, we analyse the effects of changing the aggregation strategy, and we explore the use of different rule definitions, including or not the possibility to assign weighting factors to the generated rules. We test different rule weighting heuristics at this respect. The notion of rule conflict introduced in earlier versions of the algorithm is also reviewed in the context of the various resulting configurations of the fuzzy inference engine. A generalized version of the algorithm therefore results, bringing more flexibility to the configuration of the fuzzy inference engine, and improving the performance for certain problems. The main objective is to complement the results of previous approaches by offering a comprehensive overview of this popular algorithm for fuzzy rule induction in the context of classification problems. Several well‐known machine learning classification benchmarks are analysed and compared looking for the best possible model configuration.

show abstract

CHI-BD: A fuzzy rule-based classification system for Big Data classification problems

Cited by 71 publications

References 29 publications

Why Linguistic Fuzzy Rule Based Classification Systems perform well in Big Data Applications?

Why Linguistic Fuzzy Rule Based Classification Systems perform well in Big Data Applications?

Do we still need fuzzy classifiers for Small Data in the Era of Big Data?

Revisiting the Wang–Mendel algorithm for fuzzy classification

Contact Info

Product

Resources

About