Stratified prototype selection based on a steady-state memetic algorithm: a study of scalability

Derrac, Joaquín; García, Salvador; Herrera, Francisco

doi:10.1007/s12293-010-0048-1

Cited by 31 publications

(19 citation statements)

References 45 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Similar principles can be applied to other data mining methods and problems [50]. One of the problems with stratification is determining the optimum the size of the strata [38].…”

Section: Sampling and Data Partitioningmentioning

confidence: 95%

See 1 more Smart Citation

Scaling up data mining algorithms: review and taxonomy

García-Pedrajas

Haro-García

2012

Prog Artif Intell

View full text Add to dashboard Cite

The overwhelming amount of data that are now available in any field of research poses new problems for data mining and knowledge discovery methods. Due to this huge amount of data, most of the current data mining algorithms are inapplicable to many real-world problems. Data mining algorithms become ineffective when the problem size becomes very large. In many cases, the demands of the algorithm in terms of the running time are very large, and mining methods cannot be applied when the problem grows. This aspect is closely related to the time complexity of the method. A second problem is linked with performance; although the method might be applicable, the size of the search space prevents an efficient execution, and the resulting solutions are unsatisfactory. Two approaches have been used to deal with this problem: scaling up data mining algorithms and data reduction. However, because data reduction is a data mining task itself, this technique also suffers from scalability problems. Thus, for many problems, especially when dealing with very large datasets, the only way to deal with the aforementioned problems is to scale up the data mining algorithm. Many efforts have been made to obtain methods that can be used to scale up existing data mining algorithms. In this paper, we review the methods that have been used to address the problem of scalability. We focus on general ideas, rather than specific implementations, that can be used to provide a general view of the current approaches for scaling up data mining methods. A taxonomy of the algorithms is proposed, and many examples of different tasks are presented. Among the different techniques used for data mining, we will pay special attention to evolutionary methods, because these methods have been used very successfully in many data mining tasks.

show abstract

“…Similar principles can be applied to other data mining methods and problems [50]. One of the problems with stratification is determining the optimum the size of the strata [38].…”

Section: Sampling and Data Partitioningmentioning

confidence: 95%

“…Of course, the drawback of this approach is the likely decrease in the performance of the algorithm. Derrac et al [38] used stratification to scale up a steady-state memetic algorithm for instance selection. Similar principles can be applied to other data mining methods and problems [50].…”

Section: Sampling and Data Partitioningmentioning

confidence: 99%

Scaling up data mining algorithms: review and taxonomy

García-Pedrajas

Haro-García

2012

Prog Artif Intell

View full text Add to dashboard Cite

show abstract

“…Despite the promising results shown by PR techniques with small and medium data sets, they lack of scalability to address big T R data sets (from tens of thousands of instances onwards [29]). The main problems found to deal with large-scale data are:…”

Section: Prototype Reduction and Big Datamentioning

confidence: 99%

“…Then, it joins each partial reduced set into a global solution. This approach has been used for instance selection [28,29] and generation [30] with promising results. However, two main problems appear when we increase the data set size:…”

Section: Introductionmentioning

confidence: 99%

MRPR: A MapReduce solution for prototype reduction in big data classification

et al. 2015

Self Cite

View full text Add to dashboard Cite

In the era of big data, analyzing and extracting knowledge from large-scale data sets is a very interesting and challenging task. The application of standard data mining tools in such data sets is not straightforward. Hence, a new class of scalable mining method that embraces the huge storage and processing capacity of cloud platforms is required. In this work, we propose a novel distributed partitioning methodology for prototype reduction techniques in nearest neighbor classification. These methods aim at representing original training data sets as a reduced number of instances. Their main purposes are to speed up the classification process and reduce the storage requirements and sensitivity to noise of the nearest neighbor rule. However, the standard prototype reduction methods cannot cope with very large data sets. To overcome this limitation, we develop a MapReduce-based framework to distribute the functioning of these algorithms through a cluster of computing elements, proposing several algorithmic strategies to integrate multiple partial solutions (reduced sets of prototypes) into a single one. The proposed model enables prototype reduction algorithms to be applied over big data classification problems without significant accuracy loss. We test the speeding up capabilities of our model with data sets up to 5.7 millions of Email addresses: triguero@decsai.ugr.es (Isaac Triguero), dperalta@decsai.ugr.es (Daniel Peralta), jaume.bacardit@newcastle.ac.uk (Jaume Bacardit), sglopez@ujaen.es (Salvador García), herrera@decsai.ugr.es (Francisco Herrera) March 3, 2014 instances. The results show that this model is a suitable tool to enhance the performance of the nearest neighbor classifier with big data. Preprint submitted to Neurocomputing

show abstract

“…Despite their bright performance with medium size problems, they lack of scalability with big data sets (from tens of thousands of instances [16]). Their main problems are:…”

Section: A Prototype Generation and Big Datamentioning

confidence: 99%

A combined MapReduce-windowing two-level parallel scheme for evolutionary prototype generation

Triguero

Peralta

Bacardit

et al. 2014

2014 IEEE Congress on Evolutionary Computation (CEC)

Self Cite

View full text Add to dashboard Cite

Evolutionary prototype generation techniques have demonstrated their usefulness to improve the capabilities of the nearest neighbor classifier. They act as data reduction algorithms by generating representative points of a given problem. Their main purposes are to speed up the classification process and to reduce the storage requirements and sensitivity to noise of the nearest neighbor rule. Nowadays, with the increment of available data, the use of this kind of reduction techniques becomes more important. However, their applicability can be limited to problems with no more than tens of thousands of instances. In order to address this limitation, in this work we develop a two-level parallelization scheme for evolutionary prototype generation methods. Firstly, it distributes the functioning of these algorithms in several tasks based on a MapReduce framework. Then, for each one of these tasks (mappers), we accelerate the prototype generation process by using a windowing approach. This model enables evolutionary prototype generation algorithms to be applied over large-scale classification problems without accuracy loss. Our preliminary experiments using a dataset of 1 million instances show that this proposal is an appropriate tool to improve the performance of the nearest neighbor classifier with big data.

show abstract

Stratified prototype selection based on a steady-state memetic algorithm: a study of scalability

Cited by 31 publications

References 45 publications

Scaling up data mining algorithms: review and taxonomy

Scaling up data mining algorithms: review and taxonomy

MRPR: A MapReduce solution for prototype reduction in big data classification

A combined MapReduce-windowing two-level parallel scheme for evolutionary prototype generation

Contact Info

Product

Resources

About