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.
Specialists outperform generalists in ensemble classification.
Random Hyperboxes is a simple yet effective ensemble classifier.
In ensemble methods, the outputs of a collection of diverse classifiers are combined in the expectation that the global prediction be more accurate than the individual ones. Heterogeneous ensembles consist of predictors of different types, which are likely to have different biases. If these biases are complementary, th…
Radar-based road user classification is an important yet still challenging task towards autonomous driving applications. The resolution of conventional automotive radar sensors results in a sparse data representation which is tough to recover by subsequent signal processing. In this article, classifier ensembles origin…
ICE improves classification performance by leveraging internal patterns among instances.
Learning algorithms that aggregate predictions from an ensemble of diverse base classifiers consistently outperform individual methods. Many of these strategies have been developed in a supervised setting, where the accuracy of each base classifier can be empirically measured and this information is incorporated in the…
Vote-boosting is a sequential ensemble learning method in which the individual classifiers are built on different weighted versions of the training data. To build a new classifier, the weight of each training instance is determined in terms of the degree of disagreement among the current ensemble predictions for that i…
This paper explores how diverse neural network ensembles improve prediction accuracy and robustness against deception.
XGBOD combines unsupervised and supervised methods for better outlier detection.
Paper combines RL with classifiers to improve financial trading strategies.
This paper describes structuring data and constructing plots to explore forest classification models interactively. A forest classifier is an example of an ensemble, produced by bagging multiple trees. The process of bagging and combining results from multiple trees, produces numerous diagnostics which, with interactiv…
This paper introduces a new perspective on multi-class ensemble classification that considers training an ensemble as a state estimation problem. The new perspective considers the final ensemble classifier model as a static state, which can be estimated using a Kalman filter that combines noisy estimates made by indivi…
Optimizes feature shifts for tree ensemble reclassification.
Quantum algorithm improves ensemble classification with reduced memory and time requirements.
XEM improves multivariate time series classification with explainable models.
Methods for unsupervised anomaly detection suffer from the fact that the data is unlabeled, making it difficult to assess the optimality of detection algorithms. Ensemble learning has shown exceptional results in classification and clustering problems, but has not seen as much research in the context of outlier detecti…
This paper explores methods for combining predictions in multilabel classification.
Ensemble classifier detects pneumonia patterns in chest CT images.
Existing guarantees in terms of rigorous upper bounds on the generalization error for the original random forest algorithm, one of the most frequently used machine learning methods, are unsatisfying. We discuss and evaluate various PAC-Bayesian approaches to derive such bounds. The bounds do not require additional hold…
WRSE predicts dynamic survival distributions in ICU patients.
A new stochastic method handles ensemble creation with cost constraints.
Paper proposes FVC for functional data classification.
Empirical evidence shows that ensembles, such as bagging, boosting, random and rotation forests, generally perform better in terms of their generalization error than individual classifiers. To explain this performance, Schapire et al. (1998) developed an upper bound on the generalization error of an ensemble based on t…
This paper improves deep learning model consistency through ensemble methods.
RCAM-based ensemble combines binary classifiers using similarity and vote scheme.
Ensembling DNNs improves minority group performance, leading to fairness.
Though deep neural networks have achieved significant progress on various tasks, often enhanced by model ensemble, existing high-performance models can be vulnerable to adversarial attacks. Many efforts have been devoted to enhancing the robustness of individual networks and then constructing a straightforward ensemble…
Self-paced ensemble learning improves audio classification models.
Improved stock trading model using feature selection and ensemble learning.
FAST-DAD distills complex ensemble models into faster, more accurate individual models.
Automates zero-shot classification by scoring and weighting prompts.
Building classification models is an intrinsically practical exercise that requires many design decisions prior to deployment. We aim to provide some guidance in this decision making process. Specifically, given a classification problem with real valued attributes, we consider which classifier or family of classifiers …
Ensemble quantile classifier improves performance on high-dimensional data.
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…
Consider a binary decision making process where a single machine learning classifier replaces a multitude of humans. We raise questions about the resulting loss of diversity in the decision making process. We study the potential benefits of using random classifier ensembles instead of a single classifier in the context…
We propose and evaluate alternative ensemble schemes for a new instance based learning classifier, the Randomised Sphere Cover (RSC) classifier. RSC fuses instances into spheres, then bases classification on distance to spheres rather than distance to instances. The randomised nature of RSC makes it ideal for use in en…
Stabilizes online learning by using weighted reservoir sampling.
Ensembles improve classifier performance by reducing bias, not variance.
In this study, a novel sparsity-driven weighted ensemble classifier (SDWEC) that improves classification accuracy and minimizes the number of classifiers is proposed. Using pre-trained classifiers, an ensemble in which base classifiers votes according to assigned weights is formed. These assigned weights directly affec…
Suppose some classifiers are selected from a set of hypothesis classifiers to form an equally-weighted ensemble that selects a member classifier at random for each input example. Then the ensemble has an error bound consisting of the average error bound for the member classifiers, a term for selectivity that varies fro…
Quantum machine learning uses superposition to create a large ensemble of classifiers.
The paper verifies the robustness of classifier ensembles against randomized attacks.
In multiple instance learning, objects are sets (bags) of feature vectors (instances) rather than individual feature vectors. In this paper we address the problem of how these bags can best be represented. Two standard approaches are to use (dis)similarities between bags and prototype bags, or between bags and prototyp…
A new adaptive kNN classifier outperforms Random Forests.
The paper analyzes how multiple classifiers' disagreement and polarization affect overall accuracy.
Jointly tuning ensemble models improves performance and uncertainty calibration.
A system of nested dichotomies is a method of decomposing a multi-class problem into a collection of binary problems. Such a system recursively applies binary splits to divide the set of classes into two subsets, and trains a binary classifier for each split. Many methods have been proposed to perform this split, each …
DiwE uses regional distribution changes to create diverse ensemble classifiers for concept drift.