Conclusions We demonstrate methods to refine the use of random forest models to classify behaviours of both captive and free-living animal species. We suggest there are two main reasons for reduced accuracy when
We demonstrate methods to refine the use of random forest models to classify behaviours of both captive and free-living animal species. We suggest there are two main reasons for reduced accuracy when using a domestic counterpart to predict the behaviour of a wild species in captivity; domestication...
In the case of non-linear methods, ONEHOT is not very effective in the RF context, likely because its binary nature does not cope well with the binary decision trees learned by the Random Forest algorithm. Analysis of the best scores Many bioinformatics papers (e.g.22,23,26) that use ...
The PPP method (based on the Random Forest algorithm) registered a precision score of 0.035 compared to 0.023 for the RCA benchmark. The two methods recorded recall scores of 0.073 and 0.103, and F1 scores of 0.0476 and 0.0369, respectively (Albora et al., 2023 p. 6). 12 These rankings...
These PALSAR-FNF maps were generated by classifying ALOS-2 L-band SAR imagery using the random forest algorithm (Breiman, 2001), a popular machine-learning classification algorithm for remote sensing image analysis (Belgiu and Drăguţ, 2016). A separate classification model, with separate ...
with a median number of GPS coordinates per day per user of 96. We extract thestop eventswith an algorithm based on Hariharan and Toyama40, where a stop event is defined as a temporal sequence of GPS coordinates in a radius of Δsmeters where the user stayed for at least Δtminutes. ...
While AI can accurately extract critical features and valuable information from large amounts of data to help people complete tasks faster, there are growing concerns about the non-transparency of AI in the decision-making process. The emergence of explainable AI (XAI) has allowed humans to ...
Labelled accelerometry data were then used to train a random forest algorithm using a subset of training data (60%), with model performance assessed through the remaining data (40%) separately for each season. In order to examine trade-offs in behaviour detection with accelerometer placement, ...
for example using PLS-DA to find metabolites that distinguish two classes of interest. The problem lies in the ability of algorithms such as PLS-DA to find solutions in some spaces where no real solution exists. For example, PLS-DA can separate two groups comprised completely of random data ...
If the performance is acceptable, then prediction algorithm development can proceed using the model; otherwise the model must be re-trained. (D) The final model is used to build an algorithm to predict gene silencing efficacies of siRNA sequences whose efficacies have not been experimentally ...