A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.
Selective classification can worsen accuracy disparities between groups.
Regularizing for or against class selectivity in DNNs improves test accuracy.
Value selection reduces model size while maintaining accuracy.
Improves model calibration and selection in unsupervised domain adaptation.
A novel feature selection method for SVM improves model accuracy and interpretability.
In the context of variable selection, ensemble learning has gained increasing interest due to its great potential to improve selection accuracy and to reduce false discovery rate. A novel ordering-based selective ensemble learning strategy is designed in this paper to obtain smaller but more accurate ensembles. In part…
We propose an online method for concept driftdetection based on dynamic classifier ensemble selection. Theproposed method generates a pool of ensembles by promotingdiversity among classifier members and chooses expert ensemblesaccording to global prequential accuracy values. Unlike currentdynamic ensemble selection app…
Minimizes indecisions in selective classification to control misclassification rates.
AFS-BM improves model accuracy by dynamically selecting features.
A novel weighted feature selection method using fuzzy sets improves classification accuracy and stability.
Proposes a new stability measure for model fitting on similar feature data sets.
Solar algorithm selects variables faster and more accurately in high-dimensional data.
VC-PCR improves prediction by clustering correlated variables.
This paper reviews feature selection methods using swarm intelligence.
In this paper, we investigate a new form of automated curriculum learning based on adaptive selection of accuracy requirements, called accuracy-based curriculum learning. Using a reinforcement learning agent based on the Deep Deterministic Policy Gradient algorithm and addressing the Reacher environment, we first show …
This study improves stock price prediction for Apple Inc. using feature selection and regression models with technical indicators.
Improved selection of best outputs from LLMs for better accuracy.
Paper proposes a new method for selecting the best hierarchical forecasting approach.
FoLDTree improves oblique decision trees with ULDA, enhancing accuracy and feature selection.
We consider the problem of classifying business process instances based on structural features derived from event logs. The main motivation is to provide machine learning based techniques with quick response times for interactive computer assisted root cause analysis. In particular, we create structural features from p…
Activation functions influence behavior and performance of DNNs. Nonlinear activation functions, like Rectified Linear Units (ReLU), Exponential Linear Units (ELU) and Scaled Exponential Linear Units (SELU), outperform the linear counterparts. However, selecting an appropriate activation function is a challenging probl…
A genetic algorithm with community detection improves feature selection accuracy.
New method improves prediction accuracy in business process mining by handling concept drift.
New method selects features for big data efficiently.
Pair Hidden Markov Models (PHMMs) are probabilistic models used for pairwise sequence alignment, a quintessential problem in bioinformatics. PHMMs include three types of hidden states: match, insertion and deletion. Most previous studies have used one or two hidden states for each PHMM state type. However, few studies …
We study the problem of interference source identification, through the lens of recognizing one of 15 different channels that belong to 3 different wireless technologies: Bluetooth, Zigbee, and WiFi. We employ deep learning algorithms trained on received samples taken from a 10 MHz band in the 2.4 GHz ISM Band. We obta…
We describe a method for selecting relevant new training data for the LSTM-based domain selection component of our personal assistant system. Adding more annotated training data for any ML system typically improves accuracy, but only if it provides examples not already adequately covered in the existing data. However, …
A new method for selective classification trades off accuracy for coverage.
RACER optimizes LLM-as-judge accuracy with dynamic reasoning selection.
This paper describes the autofeat Python library, which provides scikit-learn style linear regression and classification models with automated feature engineering and selection capabilities. Complex non-linear machine learning models, such as neural networks, are in practice often difficult to train and even harder to …
Proposes a more efficient knot selection method for sparse Gaussian processes.
We propose a computationally efficient wrapper feature selection method - called Autoencoder and Model Based Elimination of features using Relevance and Redundancy scores (AMBER) - that uses a single ranker model along with autoencoders to perform greedy backward elimination of features. The ranker model is used to pri…
Selective neural network improves credit risk prediction while maintaining interpretability.
Improved online penalty selection for time series models.
When selecting a classification algorithm to be applied to a particular problem, one has to simultaneously select the best algorithm for that dataset \emph{and} the best set of hyperparameters for the chosen model. The usual approach is to apply a nested cross-validation procedure; hyperparameter selection is performed…
Motivation: Biomarker discovery from high-dimensional data is a crucial problem with enormous applications in biology and medicine. It is also extremely challenging from a statistical viewpoint, but surprisingly few studies have investigated the relative strengths and weaknesses of the plethora of existing feature sele…
A machine learning configuration refers to a combination of preprocessor, learner, and hyperparameters. Given a set of configurations and a large dataset randomly split into training and testing set, we study how to efficiently select the best configuration with approximately the highest testing accuracy when trained f…
Data selection boosts fact memorization in language models.
ARHNN method improves electricity price forecasting accuracy.
High-dimensional data in many machine learning applications leads to computational and analytical complexities. Feature selection provides an effective way for solving these problems by removing irrelevant and redundant features, thus reducing model complexity and improving accuracy and generalization capability of the…
Cost-effective feature selection improves network model choice.
Dimensionality reduction is one of the key issues in the design of effective machine learning methods for automatic induction. In this work, we introduce recursive maxima hunting (RMH) for variable selection in classification problems with functional data. In this context, variable selection techniques are especially a…
A new stock selection strategy uses combined machine learning with dynamic weighting methods.
Optimizes model training efficiency with core subset selection.
Ensemble learning that can be used to combine the predictions from multiple learners has been widely applied in pattern recognition, and has been reported to be more robust and accurate than the individual learners. This ensemble logic has recently also been more applied in feature selection. There are basically two st…
This paper improves volatility forecasting using dynamic subset selection in genetic programming.
A RL framework selects features to balance bias and accuracy dynamically.
The key issue in Dynamic Ensemble Selection (DES) is defining a suitable criterion for calculating the classifiers' competence. There are several criteria available to measure the level of competence of base classifiers, such as local accuracy estimates and ranking. However, using only one criterion may lead to a poor …