Feature selection problems have been extensively studied in the setting of linear estimation (e.g. LASSO), but less emphasis has been placed on feature selection for non-linear functions. In this study, we propose a method for feature selection in neural network estimation problems. The new ...
Feature Selection using Stochastic Gates (STG) is a method for feature selection in neural network estimation problems. The new procedure is based on probabilistic relaxation of the l0 norm of features, or the count of the number of selected features. The proposed framework simultaneously learns ei...
Each detector includes an LSTMTF model with automatic feature selection. The detectors decisions are aggregated using a majority voting scheme. Additionally, by utilizing an ensemble with simpler units positioned at various subcomponents, our proposed approach can detect deviations that might not be ...
and calculation time. However, several studies have been performed using a limited number of features without feature-selection processes based on mathematical or statistical models. These studies used the most common feature set as the input. From our experience, the variable selection process ...
LSTM uses a distinctive additive gradient structure that incorporates direct access to the forget gates' activations at every time step of the learning process. Thus, it defeats the error backflow problems with the minimum computational complexity of O(1). In the proposed work, we use LSTM for ...
Hemolytic peptides are therapeutic peptides that damage red blood cells. However, therapeutic peptides used in medical treatment must exhibit low toxicity to red blood cells to achieve the desired therapeutic effect. Therefore, accurate prediction of the
Relative humidity (RH) is one of the important processes in the hydrology cycle which is highly stochastic. Accurate RH prediction can be highly beneficial for several water resources engineering practices. In this study, extreme gradient boosting (XGBoost) approach “as a selective input parameter”...
Abstract Relative humidity (RH) is one of the important processes in the hydrology cycle which is highly stochastic. Accurate RH prediction can be highly beneficial for several water resources engineering practices. In this study, extreme gradient boosting (XGBoost) approach “as a selective input pa...
6. Feature Selection Using Genetic Algorithm There can be numerous features that identify the personality of the author: preferred words, local speech features, length of sentences, use of turns of speech, vocabulary. However, a change in these parameters leads to a change in the frequency charac...
In wrapper method, feature importance is assessed using a learning algorithm while in embedded methods the learning algorithm performs feature selection as well in which feature selection and parameter selection space are searched simultaneously. We will use regression and ensembles based methods to ...