Sentence examples for supervised solution from inspiring English sources
In this section, we first analyze the supervised approaches based on big data technologies, and later we compare the best supervised solution with the classical unsupervised methods.
In general, the ROS (RS: 100%) + SVM-BD (regParam: 0.5) classifier can be considered the best supervised solution considering both performance and time.
Moreover, most of the supervised solutions do not share the source code or a trained model to be used with no supervision.
For the evaluation of POD algorithms, we compare the supervised solutions and the unsupervised ones represented by the reference RBH, RSD, and OMA algorithms following the evaluation scheme in Figure 1.
The SVM-BD classifier combined with the ROS (RS: 100%) preprocessing with regulation parameter 0.5 outdid the rest of the big data supervised solutions and the popular unsupervised (RBH, RSD, and OMA) algorithms even when the supervised model was extended to datasets containing "traps" for OD algorithms.
Although the current performance of the unsupervised solution does not adequately replace manual engineering, we believe once the performance issues are addressed, it could serve as an aide in a semi-supervised solution.
Li et al. construct a supervised classifier on top of these features to provide a semi-supervised solution to a majority of unlabeled data reaching upto an F1 score of 0.631.
Noorossana et al. (2011) proposed an integrated supervised learning solution to detect the out-of-control conditions, estimate the change point when the shift occurs in the mean vector, diagnose the variables contributing to the out-of-condition and determine the direction of the shift in the mean of each contributing variable.
Hence, Co-MIML is a semi-supervised version solution, and in our experiments, we compare all methods in a semi-supervised scenario.
Tsai et al. [46] proposed a semi-supervised HDA solution called Cross-Domain Landmark Selection (CDLS).
The paper proposes a new constrained self-organizing map (SOM) to combine multiple semi-supervised clustering solutions for further enhancing the performance of ICop-Kmeans.
