Distributed Rough Set Based Feature Selection Approach to Analyse Deep and Hand-crafted Features for Mammography Mass Classification

Hamidinekoo, Azam; Dagdia, Zaineb Chelly; Suhail, Zobia; Zwiggelaar, Reyer

doi:10.1109/bigdata.2018.8621962

Cited by 4 publications

(4 citation statements)

References 19 publications

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“…It is employed on seven heterogeneous classifications to find the rank of these classifications. In (Hamidinekoo et al, 2018) internal low, mid and high‐level abstracts or features are extracted from the handcrafted features, producing a huge number of data. With the distributed RS‐based FS method, important characteristics are selected from DL‐based and handcrafted features and fed into learning models with combined and separate data methods for the classification.…”

Section: Related Workmentioning

confidence: 99%

Handling uncertainty using optimal clustering with rough sets‐based rule generation model for data classification

Bhukya

Sadanandam

2022

Expert Systems

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In recent times, MapReduce has become a popular tool for handling big data. At the same time, uncertainty is related to arbitrariness, fuzziness, ambiguity, irregularity and incomplete knowledge. In RS theory, the uncertainty behaviour of the data in the dataset of interest is managed by using upper and lower approximate sets and classification accuracy. The RS model is integrated with data clustering technique for optimal outcomes. With this motivation, this study designs an Optimal Clustering with RS‐Based Rule Generation Model (OC‐RSRGM) for data classification on MapReduce environment. The OC‐RSRGM technique aims to generate an optimal set of rules using RST for the data classification process and it involves a two‐stage process namely Optimal Fuzzy c‐Means Clustering (OFCM) and RSRGM‐based rule generation with classification. The OFCM technique is derived to eradicate the local optimal problem of the FCM (Fuzzy c‐Means) model using Barnacles Mating Optimizer (BMO). It provides the decision‐makers with all the information needed to design appropriate mechanisms to support their decision‐making activities. The Hadoop MapReduce tool is used to handle big data. The proposed method combines an FCM, BMO, RS theory to accomplish effective decision‐making. The OC‐RSRGM technique can be employed to continuous value dataset where data point does not offer any class details and it might be uncertain. To validate the performance of OC‐RSRGM technique, a detailed experimental analysis is carried out to highlights the betterment of OC‐RSRGM technique. The proposed OC‐RSRGM technique has obtained an effective outcome with the CT of 5.43 s.

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Section: Related Workmentioning

confidence: 99%

Handling uncertainty using optimal clustering with rough sets‐based rule generation model for data classification

Bhukya

Sadanandam

2022

Expert Systems

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“…Adding to this specificity, the key concept that Spark offers is a Resilient Distributed Data set (RDD), which is a set of elements that are distributed across the nodes of the used cluster that can be operated on in a parallel way. Indeed, Spark has a number of high-level libraries for working with structured data (Spark SQL 10 ), for stream processing (Spark Streaming 11 ), for machine learning (MLlib) 12 [44], and for graphs and graph-parallel computation (GraphX 13 ). Other than that, there are also many R 14 and Python 15 libraries, among others, which allow the programmers to code without writing mappers and reducers themselves.…”

Section: Parallel Computing Framework and The Mapreduce Programming Modelmentioning

confidence: 99%

“…Within the context of big data, granular computing has begun to play important roles in many application domains such as in big data processing [6] where the theory of fuzzy sets was applied to offer a novel promising processing environment, in sentiment analysis where fuzzy set theory was applied on big social data [7], in knowledge acquisition where rough set theory was applied [8], in epidemiology where rough set theory was applied as a big data mining technique [9], in mammography mass classification where rough set theory was applied to analyse deep and hand-crafted features [10], etc.…”

Section: Introductionmentioning

confidence: 99%

“…Despite the fact that these approaches avoid such an exhaustive search performed by the classical RST version, the problem of finding a globally minimal reduct remains a big challenge mainly in the context of big data, and specifically if the application domain requires it. For instance, in many real-world applications, specifically in health or medical research, it is essential to have a globally minimal reduct to better guide the decision maker and facilitate their task as revealed in [9,10]. In this context, it is important to mention that in our research study and in line with our manuscript's goals, we mainly focus on the generation of a globally minimal reduct without calling for heuristics, which makes our task more challenging in the context of big data, and hence we aim to revise Sp-RST.…”

Section: Introductionmentioning

confidence: 99%

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A Detailed Study of the Distributed Rough Set Based Locality Sensitive Hashing Feature Selection Technique

Dagdia

Zarges

2021

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In the context of big data, granular computing has recently been implemented by some mathematical tools, especially Rough Set Theory (RST). As a key topic of rough set theory, feature selection has been investigated to adapt the related granular concepts of RST to deal with large amounts of data, leading to the development of the distributed RST version. However, despite of its scalability, the distributed RST version faces a key challenge tied to the partitioning of the feature search space in the distributed environment while guaranteeing data dependency. Therefore, in this manuscript, we propose a new distributed RST version based on Locality Sensitive Hashing (LSH), named LSH-dRST, for big data feature selection. LSH-dRST uses LSH to match similar features into the same bucket and maps the generated buckets into partitions to enable the splitting of the universe in a more efficient way. More precisely, in this paper, we perform a detailed analysis of the performance of LSH-dRST by comparing it to the standard distributed RST version, which is based on a random partitioning of the universe. We demonstrate that our LSH-dRST is scalable when dealing with large amounts of data. We also demonstrate that LSH-dRST ensures the partitioning of the high dimensional feature search space in a more reliable way; hence better preserving data dependency in the distributed environment and ensuring a lower computational cost.

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Machine Learning and Image Processing for Breast Cancer: A Systematic Map

Zerouaoui

Idri

Asnaoui

2020

Trends and Innovations in Information Systems and Technologies

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Distributed Rough Set Based Feature Selection Approach to Analyse Deep and Hand-crafted Features for Mammography Mass Classification

Cited by 4 publications

References 19 publications

Handling uncertainty using optimal clustering with rough sets‐based rule generation model for data classification

Handling uncertainty using optimal clustering with rough sets‐based rule generation model for data classification

A Detailed Study of the Distributed Rough Set Based Locality Sensitive Hashing Feature Selection Technique

Machine Learning and Image Processing for Breast Cancer: A Systematic Map

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