scholarly journals DSAE – Deep Stack Auto Encoder and RCBO – Rider Chaotic Biogeography Optimization Algorithm for Big Data Classification

2021 ◽  
Author(s):  
Anilkumar V. Brahmane ◽  
B Chaitanya Krishna
Keyword(s):  
Big Data ◽  
Optimization Algorithm ◽  
Data Classification ◽  
Initial Distribution ◽  
Data Set ◽  
Knn Classification ◽  
Big Data Classification ◽  
Is Integration ◽  
Learning Machine ◽  
Agricultural Banking

In today’s era Big data classification is a very crucial and equally widely arise issue is many applications. Not only engineering applications but also in social, agricultural, banking, educational and many more applications are there in science and engineering where accurate big data classification is required. We proposed a very novel and efficient methodology for big data classification using Deep stack encoder and Rider chaotic biogeography algorithms. Our proposed algorithms are the combinations of two algorithms. First one is Rider Optimization algorithm and second one is chaotic biogeography-based optimization algorithm. So, we named it as RCBO which is integration is ROA and CBBO. Our proposed system also uses the Deep stack auto encoder for the purpose of training the system which actually produced the accurate classification. The Apache spark platform is used initial distribution of the data from master node to slave nodes. Our proposed system is tested and executed on the UCI Machine learning data set which gives the excellent results while comparing with other algorithms such as KNN classification, Extreme Learning Machine Random Forest algorithms.

Elastic extreme learning machine for big data classification

2015 ◽  
Vol 149 ◽  
pp. 464-471 ◽  
Author(s):  
Junchang Xin ◽  
Zhiqiong Wang ◽  
Luxuan Qu ◽  
Guoren Wang
Keyword(s):  
Big Data ◽  
Extreme Learning Machine ◽  
Data Classification ◽  
Big Data Classification ◽  
Learning Machine

scholarly journals Spark Architecture and Fractional Artificial Bee Colony-Chaotic Fruitfly Ride NN for Big data Classification in Internet of Things

2019 ◽  
Vol 9 (1) ◽  
pp. 542-555
Keyword(s):  
Big Data ◽  
Internet Of Things ◽  
Optimization Algorithm ◽  
Artificial Bee Colony ◽  
Smart Cities ◽  
Data Classification ◽  
Classification Model ◽  
Bee Colony ◽  
Data Framework ◽  
Big Data Classification

The typical Internet of Things (IoT) device gathers a huge amount of data specifically termed as big data framework, which transfers the collected data from the sensing layer to the information processing layer. Various big data classification methods are adopted in the industrial applications, and smart cities, but accurately classifying the data in the IoT network poses a challenging task in the research community. Therefore, an effective big data classification model using spark-based architecture is proposed in this research. The big data classification is performed at the master node using the proposed Fractional Artificial Bee Colony- Chaotic Fruitfly Rider Optimization Algorithm (FABC-CFFRideNN). The concept of fictional computing is adopted by the rider optimization algorithm (ROA) to update the position of rider groups based on success rate and the foraging behavior of fruit flies along with the rider parameters is used to enhance to performance of data classification using the proposed CFFRideNN classifier. Moreover, the proposed Fractional Artificial Bee Colony- Chaotic Fruitfly Rider Optimization Algorithm attained better performance using the metrics, namely accuracy, specificity, and sensitivity with the values of 95.382%, 95.81%, and 98.824% for training percentage without node velocity.

Feature selection based on an improved cat swarm optimization algorithm for big data classification

2016 ◽  
Vol 72 (8) ◽  
pp. 3210-3221 ◽  
Author(s):  
Kuan-Cheng Lin ◽  
Kai-Yuan Zhang ◽  
Yi-Hung Huang ◽  
Jason C. Hung ◽  
Neil Yen
Keyword(s):  
Feature Selection ◽  
Big Data ◽  
Optimization Algorithm ◽  
Data Classification ◽  
Swarm Optimization ◽  
Cat Swarm Optimization ◽  
Big Data Classification

Big data classification with optimization driven MapReduce framework

2021 ◽  
Vol 25 (2) ◽  
pp. 173-183
Author(s):  
Mujeeb Shaik Mohammed ◽  
Praveen Sam Rachapudy ◽  
Madhavi Kasa
Keyword(s):  
Big Data ◽  
Optimization Algorithm ◽  
Moving Average ◽  
Imbalanced Data ◽  
Data Classification ◽  
Bat Algorithm ◽  
True Positive Rate ◽  
Mapreduce Framework ◽  
Positive Rate ◽  
Big Data Classification

With the technical advances, the amount of big data is increasing day-by-day such that the traditional software tools face burden in handling them. Additionally, the presence of the imbalance data in the big data is a huge concern to the research industry. In order to assure the effective management of big data and to deal with the imbalanced data, this paper proposes a new optimization algorithm. Here, the big data classification is performed using the MapReduce framework, wherein the map and reduce functions are based on the proposed optimization algorithm. The optimization algorithm is named as Exponential Bat algorithm (E-Bat), which is the integration of the Exponential Weighted Moving Average (EWMA) and Bat Algorithm (BA). The function of map function is to select the features that are presented to the classification in the reducer module using the Neural Network (NN). Thus, the classification of big data is performed using the proposed E-Bat algorithm-based MapReduce Framework and the experimentation is performed using four standard databases, such as Breast cancer, Hepatitis, Pima Indian diabetes dataset, and Heart disease dataset. From, the experimental results, it can be shown that the proposed method acquired a maximal accuracy of 0.8829 and True Positive Rate (TPR) of 0.9090, respectively.

Rider Chaotic Biography Optimization-driven Deep Stacked Auto-encoder for Big Data Classification Using Spark Architecture

2021 ◽  
Vol 18 (3) ◽  
pp. 42-62
Author(s):  
Anilkumar V Brahmane ◽  
Chaitanya B Krishna
Keyword(s):  
Feature Selection ◽  
Big Data ◽  
Optimization Algorithm ◽  
Imbalanced Data ◽  
Data Classification ◽  
Software Tools ◽  
Novel Technique ◽  
Big Data Classification ◽  
Spark Framework ◽  
Day By Day

The novelty in big data is rising day-by-day in such a way that the existing software tools face difficulty in supervision of big data. Furthermore, the rate of the imbalanced data in the huge datasets is a key constraint to the research industry. Thus, this paper proposes a novel technique for handling the big data using Spark framework. The proposed technique undergoes two steps for classifying the big data, which involves feature selection and classification, which is performed in the initial nodes of Spark architecture. The proposed optimization algorithm is named rider chaotic biography optimization (RCBO) algorithm, which is the integration of the rider optimization algorithm (ROA) and the standard chaotic biogeography-based optimisation (CBBO). The proposed RCBO deep-stacked auto-encoder using Spark framework effectively handles the big data for attaining effective big data classification. Here, the proposed RCBO is employed for selecting suitable features from the massive dataset.

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