doi:10.3389/fbioe.2024.1348106Kweku, DavidVillalba, Maria I.Willaert, Ronnie G.Yantorno, Osvaldo M.Vela, Maria E.Panorska, Anna K.Kasas, SandorFrontiers in Bioengineering & Biotechnology
Our frame work focused on not only different classification schemes and feature selection algorithms, but also ensemble methods such as boosting and bagging in an effort to improve upon the accuracy of the individual classifiers. It is evident that when all sorts of machine learning and statistically...
A New Predictive Method for Classification Tasks in Machine Learning: Multi-Class Multi-Label Logistic Model Tree (MMLMT) This paper introduces a novel classification method for multi-class multi-label datasets, named multi-class multi-label logistic model tree (MMLMT). Our ap... D Birant - ...
Research in machine learning, statistics and related fields has produced a wide variety of algorithms for classification. However, most of these algorithms assume that all errors have the same cost, which is seldom the case in KDD prob- lems. Individually making each classification learner costsensi...
Recently, many researchers have gradually shifted their attention towards FS schemes for the classification. The trend can be explained by the fact that FS employed for various purpose such as improve efficiency and performance of the learning algorithm, reducing computational complexity, obtain a high...
ML promises to achieve a much higher level of classification precision than the previous methods, especially for complex geological problems characterized by a large enough number of input variables (Petrelli and Perugini2016). Moreover, ML learns the classification features by itself and does not nee...
result, but the access to training data is a real time consuming and costly process. Fortunately, the advent of big data platform could hopefully solve this problem. Considering that the big data technique has become a hot topic in vehicle field, the usage of machine learning will have ...
Problems with multiple conflicting criteria are usually modeled by the methods proposed in the field of Multi-Criteria Decision Making (MCDM). In MCDM, one of the most important topics is the weighting of criteria. On the other hand, classification is employed in numerous real-world issues, like...
firstly we use fuzzy measure to solve non-monotonic of Dempster-Shafer Evidence Theory;then we quantify the uncertainty calculation about multi-dimensional attribute information by using of new Dempster-Shafer fuzzy measure function;finally we determine FM-KNN classification rules by a sample of support...
As an important method in the field of machine learning, ensemble learning has been shown to provide significant improvement to the generalization ability of algorithms as early as in the classification and clustering tasks5,6. Introducing the idea of ensemble into anomaly detection reduces the ...