Classification of data from electronic nose using relevance vector machines

An RVM-Based Model for Assessing the Failure Probability of Slopes along the Jinsha River, Close to the Wudongde Dam Site, China

“…Considering that there is a lack of information in most related papers concerning the determination of the optimal σ for RVM, a procedure of its estimation was conducted, in which several different values of σ were checked to determine which one of them gives better classification capabilities. As landslide hazard assessment is linearly non-separate problem, a cross-validation approach is adopted for the parameter search [13,30]. The accuracy is defined as the percentage of correct predictions for slope failure probabilities among all testing slopes [30].…”

“…As landslide hazard assessment is linearly non-separate problem, a cross-validation approach is adopted for the parameter search [13,30]. The accuracy is defined as the percentage of correct predictions for slope failure probabilities among all testing slopes [30]. In this research, "correct" could be defined as follows: for a failed slope, the model's prediction is considered "correct" if the model's output is greater than 0.5; on the other hand, for a stable slope, it is considered "correct" if the model's output is less than 0.5.…”

“…In this research, "correct" could be defined as follows: for a failed slope, the model's prediction is considered "correct" if the model's output is greater than 0.5; on the other hand, for a stable slope, it is considered "correct" if the model's output is less than 0.5. In this work, the 4-fold cross-validation technique is used by dividing the training dataset into 4 folds [30]; one fold (25%) is used as the validation set and the combination of the remaining three folds (75%) is used for training. By iterating each fold as the validation set and the aggregation of other folds as the training set, four validation accuracies can be obtained.…”

“…The average of these accuracies is taken as the cross-validation accuracy (CVA) for a given σ. According to Wang et al [30], several different values of σ are considered ( Table 2). The value which yields the highest CVA is deemed to be the optimal value of σ.…”

“…Based on this algorithm, a recently developed machine learning technique, relevance vector machine (RVM), was originally introduced by Tipping [29]. As a Bayesian treatment of the sparse learning problem, the RVM can yield a probabilistic output [30].…”