Title: DEVELOPING OF MULTICLASS CLASSIFIER MODEL USING ENSEMBLE APPROACH FOR BUG REPORTS ANALYSIS
<br>Cover Date: 2025-02-01
<br>Cover Display Date: February 2025
<br>DOI: 10.24507/icicel.19.02.149
<br>Description: Prior research mostly concentrated on identifying actual-bug reports using binary classification. The information contained in those actual-bug reports can be utilized for software fixing purposes. Additional pertinent information is necessary to enhance and uphold software quality. This information from bug reports is referred to as “enhancement”. Conversely, bug reports that are relevant to the elimination, restructuring, substitution, activation, or deactivation of software functions, as well as other engineering tasks, are classified as “task”. Hence, bug report classification should encompass not only binary classification but also multiclass classification. Hence, this study focused on the issue of multiclass classification for bug reports. The proposed approach attempted to categorize bug reports into three distinct classes: actual-bug, enhancement, and task. This study developed a multiclass classifier model using ensemble method. The obtained model consists of five classifier models: Support Vector Machine (SVM) with linear, SVM with RBF, Logistic Regression, Multinomial Naïve Bayes, and eXtreme Gradient Boosting. The bug report features consist of unigrams and CamelCase words, whereas the term weighting algorithm employed is term frequency-inverse gravity moment (tf-igm). This study utilized two bug report datasets, specifically from FireFox and Thunderbird, which were acquired using the Bugzilla system. Also, the proposed model was compared to two prior models considered as the baselines. In comparison to the baseline models, the accuracy, F1, and AUC scores of the proposed model were marginally higher.
<br>Citations: 0
<br>Aggregation Type: Journal
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<br><br><br>Title: Predicting the Close-price of Cryptocurrency Using the Kernel Regression Algorithm
<br>Cover Date: 2023-01-01
<br>Cover Display Date: 2023
<br>DOI: 10.1109/RI2C60382.2023.10356032
<br>Description: The aim of this work is to utilize the kernel regression (KR) approach to predict the closed-price for cryptocurrencies. This study makes use of three datasets: Bitcoin (BTC), Litecoin (LTC), and Ethereum (ETH). The min-max normalization method was used to scale feature values to a common range, often between 0 and 1. Furthermore, support vector regression (SVR) and long-short term memory (LSTM) were used to compare the prediction model-based on KR. The result of the KR models utilizing RMSE and MAPE demonstrated that the predictive model-based on KR gave more satisfying results.
<br>Citations: 1
<br>Aggregation Type: Conference Proceeding
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<br><br><br>Title: Machine Learning-based Multiclass Classification Methods for Sentiment Analysis
<br>Cover Date: 2023-01-01
<br>Cover Display Date: 2023
<br>DOI: 10.1109/InCIT60207.2023.10413035
<br>Description: Sentiment analysis, also known as opinion mining, is the process of identifying the sentiment or emotion conveyed in a textual review. This requires categorizing the expressed opinions into several sentiment classes, namely positive, negative, or neutral. Typically, machine learning algorithms are employed to construct a sentiment classifier, which is subsequently utilized to automatically assign appropriate sentiment to individual textual reviews. Numerous machine learning methods have been utilized for these purposes. Determining the most suitable algorithm for sentiment analysis is a challenge. One potential methodology is doing a comparative examination of the algorithm's performance with the dataset under consideration, and then choose the most optimal sentiment classifier for adoption. In this study, we conducted a comparative analysis of many machine learning algorithms with lexicon-based approach, including multinomial naïve bayes, support vector machine, k-nearest neighbors, random forest and an ensemble approach combining these algorithms, with the purpose of developing a sentiment classifier model using the TripAdvisor dataset. The objective was to classify hotel customer reviews into three distinct categories: positive, neutral, and negative. After evaluation of recall, precision, F1, and accuracy metrics, it can be concluded that the ensemble approach yields superior outcomes compared to other approaches.
<br>Citations: 0
<br>Aggregation Type: Conference Proceeding
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