Industrial Quality Prediction System through Data Mining Algorithm
Volume-3 | Issue-2
Comparative Analysis an Early Fault Diagnosis Approaches in Rotating Machinery by Convolution Neural Network
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Nakagami-m Fading Detection with Eigen Value Spectrum Algorithms
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Abstractive Summarization System
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Design of Adaptive Estimator for Nonlinear control system in Noisy Domain
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Automated Nanopackaging using Cellulose Fibers Composition with Feasibility in SEM Environment
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Comparative Analysis of Temperature Measurement Methods based on Degree of Agreement
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Transistor Sizing using Hybrid Reinforcement Learning and Graph Convolution Neural Network Algorithm
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A Review on Meshing Techniques in Biomedicine
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EL DAPP - An Electricity Meter Tracking Decentralized Application
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SMART STREET SYSTEM WITH IOT BASED STREET LIGHT OPERATION AND PARKING APPLICATION
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ENERGY AND POWER EFFICIENT SYSTEM ON CHIP WITH NANOSHEET FET
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Abstractive Summarization System
Volume-3 | Issue-4
A Review on Meshing Techniques in Biomedicine
Volume-3 | Issue-4
MIMO BASED HIGH SPEED OPTICAL FIBER COMMUNICATION SYSTEM
Volume-1 | Issue-2
Industrial Quality Prediction System through Data Mining Algorithm
Volume-3 | Issue-2
Comparative Analysis of Temperature Measurement Methods based on Degree of Agreement
Volume-3 | Issue-3
Transistor Sizing using Hybrid Reinforcement Learning and Graph Convolution Neural Network Algorithm
Volume-3 | Issue-3
VIRTUAL REALITY SIMULATION AS THERAPY FOR POSTTRAUMATIC STRESS DISORDER (PTSD)
Volume-1 | Issue-1
Comparative Analysis an Early Fault Diagnosis Approaches in Rotating Machinery by Convolution Neural Network
Volume-3 | Issue-2
Volume - 2 | Issue - 2 | june 2020
Published
06 June, 2020
Imbalanced data refers to a problem in machine learning where there exists unequal distribution of instances for each classes. Performing a classification task on such data can often turn bias in favour of the majority class. The bias gets multiplied in cases of high dimensional data. To settle this problem, there exists many real-world data mining techniques like over-sampling and under-sampling, which can reduce the Data Imbalance. Synthetic Minority Oversampling Technique (SMOTe) provided one such state-of-the-art and popular solution to tackle class imbalancing, even on high-dimensional data platform. In this work, a novel and consistent oversampling algorithm has been proposed that can further enhance the performance of classification, especially on binary imbalanced datasets. It has been named as NMOTe (Navo Minority Oversampling Technique), an upgraded and superior alternative to the existing techniques. A critical analysis and comprehensive overview on the literature has been done to get a deeper insight into the problem statements and nurturing the need to obtain the most optimal solution. The performance of NMOTe on some standard datasets has been established in this work to get a statistical understanding on why it has edged the existing state-of-the-art to become the most robust technique for solving the two-class data imbalance problem.
Keywordsimbalanced data machine learning classification data mining over-sampling under-sampling SMOTe NMOTe
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