Results 11 to 20 of about 12,774 (258)
DTO-SMOTE: Delaunay Tessellation Oversampling for Imbalanced Data Sets
Information, 2020 One of the significant challenges in machine learning is the classification of imbalanced data. In many situations, standard classifiers cannot learn how to distinguish minority class examples from the others.
Alexandre M. de Carvalho +1 more
doaj +1 more source
Machine learning approach to customer sentiment analysis in twitter airline reviews [PDF]
E3S Web of Conferences, 2023 Customers typically provide both online and physical services they use ratings and reviews. However, the volume of reviews might grow very quickly. The power of machine learning to recognize this kind of data is astounding. Numerous algorithms that could
Pujo Ariesanto Akhmad Ekka +2 more
doaj +1 more source
Barekeng, 2023 Classification is a supervised learning method that predicts the class of objects whose labels are unknown. Classification in machine learning will produce good performance if it has a balanced data class on the response variable.
Nila Lestari +3 more
doaj +1 more source
Barekeng, 2023 Anemia and iron deficiency are health problems in Indonesia and globally. In Multiclass Classification, data problems often occur, such as missing data, too many variables, and unbalanced data.
Fathu Nurrahman +3 more
doaj +1 more source
PREDICTION OF SURVIVAL OF HEART FAILURE PATIENTS USING RANDOM FOREST
Pilar Nusa Mandiri, 2020 Human survival, one of the roles that is controlled by the heart, makes the heart need to be guarded and be aware of its damage. Heart failure is the final stage of all heart disease.
Sri Rahayu +5 more
doaj +1 more source
Analysis of Stroke Classification Using Random Forest Method
Ilkom Jurnal Ilmiah, 2022 Stroke is a disease in which the sufferer experiences or experiences a rupture of a blood vessel in the brain so that the brain does not get a blood supply that provides oxygen.
Muhammad Firdaus Banjar +3 more
doaj +1 more source
, 2013 Several real world prediction problems involve forecasting rare values of a target variable. When this variable is nominal we have a problem of class imbalance that was already studied thoroughly within machine learning. For regression tasks, where the target variable is continuous, few works exist addressing this type of problem.
Luís Torgo +3 more
openaire +1 more source
Ilkom Jurnal Ilmiah, 2023 Nowadays, information technology especially machine learning has been used to evaluate the feasibility of debtors. One of the challenges in this classification model is the occurrence of imbalanced datasets, especially in the German Credit Dataset ...
Edi Priyanto +3 more
doaj +1 more source
Implementasi SMOTE dan Under Sampling pada Imbalanced Dataset untuk Prediksi Kebangkrutan Perusahaan
Jurnal Komputer Terapan, 2021 Kebangkrutan pada suatu perusahaan menjadi masalah yang serius karena dapat menyebabkan kerusakan ekonomi serta konsekuensi sosial lainnya. Sangat penting untuk melakukan prediksi kebangkrutan sedini mungkin karena prediksi ini dapat bermanfaat untuk ...
Wilda Imama Sabilla +1 more
doaj +1 more source
A-SMOTE: A New Preprocessing Approach for Highly Imbalanced Datasets by Improving SMOTE
International Journal of Computational Intelligence Systems, 2019 Imbalance learning is a challenging task for most standard machine learning algorithms. The Synthetic Minority Oversampling Technique (SMOTE) is a well-known preprocessing approach for handling imbalanced datasets, where the minority class is oversampled by producing synthetic examples in feature vector rather than data space.
Ahmed Saad Hussein +3 more
openaire +2 more sources