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An Imbalanced Dataset and Class Overlapping Classification Model for Big Data

Computer Systems Science & Engineering 2023年第2期44卷 1009-1024页

作者： mini prince P.M.Joe Prathap Department of Information Technology St.Peter’s College of Engineering and TechnologyChennai600054TamilnaduIndia Department of Information Technology R.M.D Engineering CollegeChennai601206TamilnaduIndia

Most modern technologies,such as social media,smart cities,and the internet of things(IoT),rely on big *** big data is used in the real-world applications,two data challenges such as class overlap and class imbalance *** dealing with large datasets,most traditional classiﬁers are stuck in the local optimum *** a result,it’s necessary to look into new methods for dealing with large data *** solutions have been proposed for overcoming this *** rapid growth of the available data threatens to limit the usefulness of many traditional *** such as oversampling and undersampling have shown great promises in addressing the issues of class *** all of these techniques,Synthetic Minority Oversampling TechniquE(SMOTE)has produced the best results by generating synthetic samples for the minority class in creating a balanced *** issue is that their practical applicability is restricted to problems involving tens of thousands or lower instances of *** this paper,we have proposed a parallel mode method using SMOTE and MapReduce strategy,this distributes the operation of the algorithm among a group of computational nodes for addressing the aforementioned *** proposed solution has been divided into three ***ﬁrst stage involves the process of splitting the data into different blocks using a mapping function,followed by a pre-processing step for each mapping block that employs a hybrid SMOTE algo-rithm for solving the class imbalanced *** each map block,a decision tree model would be ***,the decision tree blocks would be com-bined for creating a classiﬁcation *** have used numerous datasets with up to 4 million instances in our experiments for testing the proposed scheme’s *** a result,the Hybrid SMOTE appears to have good scalability within the framework proposed,and it also cuts down the processing time.

关键词： Imbalanced dataset class overlapping SMOTE MapReduce parallel programming oversampling

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A Novel Approach to Design Distribution Preserving Framework for Big Data

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Intelligent Automation & Soft Computing 2023年第3期35卷 2789-2803页

In several fields like financial dealing,industry,business,medicine,et cetera,Big Data(BD)has been utilized extensively,which is nothing but a collection of a huge amount of ***,it is highly complicated along with time-consuming to process a massive amount of ***,to design the Distribution Preserving Framework for BD,a novel methodology has been proposed utilizing Manhattan Distance(MD)-centered Partition Around Medoid(MD–PAM)along with Conjugate Gradient Artificial Neural Network(CG-ANN),which undergoes various steps to reduce the complications of ***,the data are processed in the pre-processing phase by mitigating the data repetition utilizing the map-reduce function;subsequently,the missing data are handled by substituting or by ignoring the missed *** that,the data are transmuted into a normalized ***,to enhance the classification performance,the data’s dimensionalities are minimized by employing Gaussian Kernel(GK)-Fisher Discriminant Analysis(GK-FDA).Afterwards,the processed data is submitted to the partitioning phase after transmuting it into a structured *** the partition phase,by utilizing the MD-PAM,the data are partitioned along with grouped into a ***,by employing CG-ANN,the data are classified in the classification phase so that the needed data can be effortlessly retrieved by the *** analogize the outcomes of the CG-ANN with the prevailing methodologies,the NSL-KDD openly accessible datasets are *** experiential outcomes displayed that an efficient result along with a reduced computation cost was shown by the proposed *** proposed work outperforms well in terms of accuracy,sensitivity and specificity than the existing systems.

关键词： Big data artiﬁcial neural network ﬁsher discriminant analysis distribution preserving framework manhattan distance

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