Oversampling technique in student performance classification from engineering course

Rachburee, Nachirat; Punlumjeak, Wattana

doi:10.11591/ijece.v11i4.pp3567-3574

Cited by 11 publications

(9 citation statements)

References 22 publications

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“…The results of this study were tested with three scenarios. Each scenario is distinguished from the content of the dataset used, namely 1) using data without under-sampling or over-sampling techniques in the first scenario, then 2) without using the unclassified class and over-sampling the minority class in the second scenario, and 3) using the entire class with both under-sampling for the majority class and over-sampling (SMOTE) [22] in the minority class. An unclassified class is a class that contains patterns that do not belong to the ten (10) candlestick patterns.…”

Section: Resultsmentioning

confidence: 99%

Candlestick Pattern Classification Using Feedforward Neural Network

Karmelia¹,

Widjaja²,

Hansun³

2022

ijasca

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Investment in the capital market can help boost a country’s economic growth. Without a doubt, in investing, a technical analysis of the condition of the stock is needed at that time. One of the technical analyses that can be done is to look at the historical data of stocks. Candlestick charts can summarize historical data that contain price value for Open, High, Low, and Close (OHLC) in the form of a chart. A group of candlesticks will form a pattern that can help investors to see whether the stock is trending up or down. The number of candlestick patterns and the manual determination of candlestick patterns may take time and effort. Feedforward Neural Network (FNN) is one of the algorithms that can help map the input and output of a given dataset. This study aims to implement FNN to classify candlestick patterns found in historical stock data. The test results show that the accuracy for each model scenario does not guarantee whether all patterns can be properly recognized. This is mainly caused by an imbalanced dataset and the classification process cannot be done properly. Testing with the original data has an accuracy of above 85% on each stock, but the average F1-score is below 45%. Further experiments using random under-sampling and Synthetic Minority Oversampling Technique (SMOTE) result in decreased accuracy value, where the lowest is 59% in PT Bukit Asam Tbk share, and an increased average F1-score, but less than 15%. Keywords: Candlestick patterns, feedforward neural network, investment, historical data, OHLC, SMOTE, stocks.

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Section: Resultsmentioning

confidence: 99%

Candlestick Pattern Classification Using Feedforward Neural Network

Karmelia¹,

Widjaja²,

Hansun³

2022

ijasca

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“…In machine learning, a decision tree (DT) is a well-known classification method [71]- [76]. Numerous advantages exist for using DT in classification, including superior interpretability, scalability, and the ability to express in both graphical and textual formats [19], [77]- [79]. The three most well-known decision tree learning algorithms are ID3, C4.5, and CART.…”

Section: Decision Tree: Id3 Algorithmmentioning

confidence: 99%

Breast cancer disease classification using fuzzy-ID3 algorithm based on association function

Idris

Ismail

Mohamad

et al. 2022

IJ-AI

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Breast cancer is the second leading cause of mortality among female cancer patients worldwide. Early detection of breast cancer is considerd as one of the most effective ways to prevent the disease from spreading and enable human can make correct decision on the next process. Automatic diagnostic methods were frequently used to conduct breast cancer diagnoses in order to increase the accuracy and speed of detection. The fuzzy-ID3 algorithm with association function implementation (FID3-AF) is proposed as a classification technique for breast cancer detection. The FID3-AF algorithm is a hybridisation of the fuzzy system, the iterative dichotomizer 3 (ID3) algorithm, and the association function. The fuzzy-neural dynamicbottleneck-detection (FUZZYDBD) is considered as an automatic fuzzy database definition method, would aid in the development of the fuzzy database for the data fuzzification process in FID3-AF. The FID3-AF overcame ID3’s issue of being unable to handle continuous data. The association function is implemented to minimise overfitting and enhance generalisation ability. The results indicated that FID3-AF is robust in breast cancer classification. A thorough comparison of FID3-AF to numerous existing methods was conducted to validate the proposed method’s competency. This study established that the FID3-AF performed well and outperform other methods in breast cancer classification.

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“…In other words, accuracy will not give a clear picture of the classifier's performance in an imbalanced dataset. Issues of imbalanced data occurred in many fields such as bankruptcy risk data [2], credit scoring [3], healthcare medical data [4], student performance [5], point cloud data [6], anomalies detection [7] and also water quality data [8]. In real-world applications, the severity of class imbalance may range from mild to severe [9].…”

Section: Introductionmentioning

confidence: 99%

Comparison of ensemble hybrid sampling with bagging and boosting machine learning approach for imbalanced data

Malek

Yaacob

Wah

et al. 2022

IJEECS

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Training an imbalanced dataset can cause classifiers to overfit the majority class and increase the possibility of information loss for the minority class. Moreover, accuracy may not give a clear picture of the classifier’s performance. This paper utilized decision tree (DT), support vector machine (SVM), artificial neural networks (ANN), K-nearest neighbors (KNN) and Naïve Bayes (NB) besides ensemble models like random forest (RF) and gradient boosting (GB), which use bagging and boosting methods, three sampling approaches and seven performance metrics to investigate the effect of class imbalance on water quality data. Based on the results, the best model was gradient boosting without resampling for almost all metrics except balanced accuracy, sensitivity and area under the curve (AUC), followed by random forest model without resampling in term of specificity, precision and AUC. However, in term of balanced accuracy and sensitivity, the highest performance was achieved by random forest with a random under-sampling dataset. Focusing on each performance metric separately, the results showed that for specificity and precision, it is better not to preprocess all the ensemble classifiers. Nevertheless, the results for balanced accuracy and sensitivity showed improvement for both ensemble classifiers when using all the resampled dataset.

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Oversampling technique in student performance classification from engineering course

Cited by 11 publications

References 22 publications

Candlestick Pattern Classification Using Feedforward Neural Network

Candlestick Pattern Classification Using Feedforward Neural Network

Breast cancer disease classification using fuzzy-ID3 algorithm based on association function

Comparison of ensemble hybrid sampling with bagging and boosting machine learning approach for imbalanced data

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