Improving multiclass classification and outlier detection method through ensemble technique

Ndirangu, Dalton; Mwangi, Waweru; Nderu, Lawrence

doi:10.1145/3290420.3290450

Cited by 2 publications

(2 citation statements)

References 13 publications

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“…2) Random Subspace Algorithm (RNDS): The base classifier model is based on a set constructed from the initial set of functionalities using the RNDS approach proposed by Ho [23]. Through a simple majority vote procedure, the outcomes of the individual graders are merged in a final decision.…”

Section: ) Decisionmentioning

confidence: 99%

RHEM: A Robust Hybrid Ensemble Model for Students’ Performance Assessment on Cloud Computing Course

Sakri¹,

Alluhaidan²

2020

IJACSA

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Creating tools, such as a prediction model to assist students in a traditional or virtual setting, is an essential activity in today's educational climate. The early stage towards incorporating these predictive models using techniques of machine learning focused on predicting the achievement of students in terms of the grades obtained. The research aim is to propose a robust hybrid ensemble model (RHEM) that can warn at-risks students (on Cloud Computing course) of their likely outcomes at the early semester assessment. We hybridised four renowned single algorithms-Naïve Bayes, Multilayer Perceptron, k-Nearest Neighbours, and Decision Tablewith four well-established ensemble algorithms-Bagging, RandomSubSpace, MultiClassClassifier, and Rotation Forestwhich produced 16 new hybrid ensemble classifier models. Hence, we have thoroughly and rigorously built, trained, and tested 24 models all together. The experiment concluded that the Rotation Forest + MultiLayer Perceptron model was the best performing model based on the model evaluation in terms of Accuracy (91.70%), Precision (86.1%), F-Score rate (87.3%), and Receiver Operating Characteristics Area detection (98.6%). Our research will help students identify their likely final grades in terms of whether they are excellent, very good, good, pass, or fail, and, thus, transform their academic conduct to achieve higher grades in the final exam accordingly.

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Section: ) Decisionmentioning

confidence: 99%

RHEM: A Robust Hybrid Ensemble Model for Students’ Performance Assessment on Cloud Computing Course

Sakri¹,

Alluhaidan²

2020

IJACSA

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“…The classifier methods and clustering methods could also be used to handle outliers in a dataset. Classification has been one of the essential methods for outlier detection [12,13]. In [14], an adaptive distributed Bayesian approach was proposed.…”

Section: Introductionmentioning

confidence: 99%

A rapid outliers detection and correction method for external ballistic velocity measurement data

Yan,

Qian,

et al. 2023

Meas. Sci. Technol.

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Due to the inﬂuence of various sudden and abnormal factors during the tracking environment, equipment working conditions, and operation process, there are inevitable outliers in the tracking measurement process of aircraft such as carrier rockets, artiﬁcial satellites, and missiles. Therefore, the prerequisite for ensuring the reliability of processing results is to timely and accurately detect and correct outliers. In this paper, we proposed the sliding window-based variable degree B-spline function method (SWVD B-spline), which can handle isolated outliers and spotted outliers. SWVD B-spline uses variable degree B-spline function to model observed data in sliding windows, which can detect and correct outliers point by point along with window sliding. Then, we propose an initial window data selection method to remove outliers in initial windows to ensure the processing eﬀect. In addition, because there are often inﬂection points in external ballistic velocity measurement data, diﬀerential evolution is used to optimize variable degree B-spline in windows that include inﬂection points to improve processing accuracy. The experimental results verify that SWVD B-spline can handle various outliers rapidly and eﬃciently.

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Improving multiclass classification and outlier detection method through ensemble technique

Cited by 2 publications

References 13 publications

RHEM: A Robust Hybrid Ensemble Model for Students’ Performance Assessment on Cloud Computing Course

RHEM: A Robust Hybrid Ensemble Model for Students’ Performance Assessment on Cloud Computing Course

A rapid outliers detection and correction method for external ballistic velocity measurement data

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