A Survey of Methods for Managing the Classification and Solution of Data Imbalance Problem

Hasib, Khan Md.; Iqbal, Md. Sadiq; Shah, Faisal Muhammad; Mahmud, Jubayer Al; Popel, Mahmudul Hasan; Showrov, Md. Imran Hossain; Ahmed, Shakil; Rahman, Obaidur

doi:10.3844/jcssp.2020.1546.1557

Cited by 89 publications

(20 citation statements)

References 48 publications

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“…We have used one more evaluation metric, the fraction of correct matches. It is defned as the percentage of the number of times the best match I s detects the correct loop [36]. fraction of correct matches � number of correct predicted places number of total evaluated places × 100%.…”

Section: Evaluation Methodmentioning

confidence: 99%

Learning Condition-Invariant Scene Representations for Place Recognition across the Seasons Using Auto-Encoder and ICA

Islam

Rahman

2022

Journal of Electrical and Computer Engineering

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To localize the position and perfect autonomous navigation, building up a map is essential for mobile robots. The map becomes very important when the weather is not appropriate for the robot. However, the map becomes inconsistent when the robot moves in the environment and detects errors with emotional accuracy. The loop-closure detection is the process through which a robot can acknowledge the location visited previously, which can identify the ultimate solution to the previous problem. The robot faced difficulty identifying its previously visited path when the environment underwent an extreme change. The main motive of our work is to promote a model capable of understanding the scenes that are presented robustly. Moreover, during seasonal changes, this model provides an appropriate loop-closure detection result. Independent component analysis (ICA) and auto-encoder are proposed to complete our research work. ICA is a powerful tool to describe invariant images perfectly. Especially, when the robot moves through a changing environment, ICA provided more accurate outcomes than the other algorithm (baseline algorithm). On the other hand, the auto-encoder can distinguish between two features of scene variant condition and invariant condition. The encoder takes our work’s next steps by discovering possible routes. To analyze the performance, this work uses the baseline method with a precision-recall curve and a fraction of correct matches. The proposed algorithm ICA showed a 91.05% accuracy rate, which is better than the baseline algorithms, and the appropriate route-finding rate using an auto-encoder is also acceptable.

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Section: Evaluation Methodmentioning

confidence: 99%

Learning Condition-Invariant Scene Representations for Place Recognition across the Seasons Using Auto-Encoder and ICA

Islam

Rahman

2022

Journal of Electrical and Computer Engineering

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“…One of the most popular machine learning algorithms, Support Vector Machine (SVM) [12], offers adequate accuracy while requiring less processing power. This algorithm discovers a hyper plane to categorize data points in an N-dimensional space with the greatest possible margin, or the distance between data points of the various classes [13]. The classification's decision boundary is the hyperplane.…”

Section: 13the Support Vector Machinementioning

confidence: 99%

Deep Learning and Multiclass Machine Learning Classifier Approach for Predicting Primary Tumors

-¹,

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2023

IJFMR

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Deep Learning (DL) and Machine Learning (ML) have the great prospect to play a significant role in the medical field in disease prediction. The tumor or cancer is one of the major health issues that each nation is currently dealing with, and it is the topic of this essay. The prediction of unidentified primary tumors in the dataset is delineated in this paper. Given that it provides significantly higher accuracy than binary classifiers, different multiclass classifier such as K-Nearest Neighbor (KNN), CatBoost Classifier, Random Forest Classifier, Gradient Boosting Classifier, Light Gradient Boosting Machine, Ada Boost Classifier, Decision Tree Classifier, SVM - Linear Kernel, Naive Bayes and Deep neural networks (DNN1, DNN2, and DNN3) are used to categorize multiclass datasets available in the UCI machine learning repository. Among the stated machine learning classifiers, the k-Nearest Neighbor (KNN) had the highest classification accuracy of 92.92%. The three layer deep neural network (DNN2), among deep learning techniques, had produced the best accuracy of 97.66% using the chosen features as input. The gathered results from this work showed that deep neural networks outperformed machine learning techniques.

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“…The training data will keep increasing as users keep using the system and new gesture instances will be added through self adaptation with lower likelihood values. Moreover, the number of adapted instances will be much greater than the original gesture instances which can lead to data imbalance problem [39]. There are several ways to classify imbalanced data [40].…”

Section: Load Balancingmentioning

confidence: 99%

3D Gesture Recognition and Adaptation for Human–Robot Interaction

et al. 2022

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Gesture-based human-robot interaction has been an important area of research in recent years. The primary aspect for the researchers has always been to create a gesture detection system that is insensitive to lighting and backdrop surroundings. This research proposes a 3D gesture recognition and adaption system based on Kinect for human-robot interaction. The framework consists of four modules, i.e., pointing gesture recognition, 3D dynamic gesture recognition, gesture adaptation, and robot navigation. The proposed dynamic gesture recognition module employs three distinct classifiers: HMM, Multi-class SVM, and CNN. The adaptation module can adapt to new and unrecognized gestures applying semi-supervised self-adaptation or user consent-based adaptation. A Graphical User Interface (GUI) is built for training and testing the proposed system on the fly. A simple simulator along with two different robot-navigation algorithms are developed to test robot navigation based on the recognized gestures. The framework is trained and tested through a five-fold cross-validation method with a total of 3,600 gesture instances of ten predefined gestures performed by 24 persons (Three age categories: Young, Middle Aged, Adult; each with 1,200 gestures). The proposed system achieves a maximum accuracy score of 95.67% with HMM for Middle Aged category, 92.59% with SVM for Middle Aged category, and 89.58% with CNN for Young category in dynamic gesture recognition. Considering all the three age categories, the system achieves an average accuracy of 94.61%, 91.95%, and 88.97% in recognizing dynamic gestures with HMM, SVM, and CNN respectively. Moreover, the system recognizes pointing gestures in real-time.

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A Survey of Methods for Managing the Classification and Solution of Data Imbalance Problem

Abstract: This open access article is distributed under a Creative Commons Attribution (CC-BY) 3.0 license.

Cited by 89 publications

References 48 publications

Learning Condition-Invariant Scene Representations for Place Recognition across the Seasons Using Auto-Encoder and ICA

Learning Condition-Invariant Scene Representations for Place Recognition across the Seasons Using Auto-Encoder and ICA

Deep Learning and Multiclass Machine Learning Classifier Approach for Predicting Primary Tumors

3D Gesture Recognition and Adaptation for Human–Robot Interaction

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