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.
Efficient neural network ensembles improve image classification reliability and uncertainty quantification.
RCAM-based ensemble combines binary classifiers using similarity and vote scheme.
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…
Self-paced ensemble learning improves audio classification models.
Tree ensembles like RF and GBT can be seen as kernels, improving regression and classification performance.
Ensemble learning, the machine learning paradigm where multiple algorithms are combined, has exhibited promising perfomance in a variety of tasks. The present work focuses on unsupervised ensemble classification. The term unsupervised refers to the ensemble combiner who has no knowledge of the ground-truth labels that …
This paper explores methods for combining predictions in multilabel classification.
As data streams become more prevalent, the necessity for online algorithms that mine this transient and dynamic data becomes clearer. Multi-label data stream classification is a supervised learning problem where each instance in the data stream is classified into one or more pre-defined sets of labels. Many methods hav…
Study pitfalls of deep learning ensembles in uncertainty estimation.
A new ensemble method using random projections for kNN classification.
Ensembles improve classifier performance by reducing bias, not variance.
In recent years, multi-label classification problem has become a controversial issue. In this kind of classification, each sample is associated with a set of class labels. Ensemble approaches are supervised learning algorithms in which an operator takes a number of learning algorithms, namely base-level algorithms and …
JUCAL jointly calibrates aleatoric and epistemic uncertainties in classifier ensembles.
Bayesian inference using stochastic neural networks ensembles.
Ensembles, as a widely used and effective technique in the machine learning community, succeed within a key element -- "diversity." The relationship between diversity and generalization, unfortunately, is not entirely understood and remains an open research issue. To reveal the effect of diversity on the generalization…
XEM improves multivariate time series classification with explainable models.
Ensemble techniques are powerful approaches that combine several weak learners to build a stronger one. As a meta learning framework, ensemble techniques can easily be applied to many machine learning techniques. In this paper we propose a neural network extended with an ensemble loss function for text classification. …
Ensemble GP improves genetic programming by achieving better results with smaller models.
Specialists outperform generalists in ensemble classification.
Paper proposes ECOC for deep neural network ensembles to improve performance.
Recognizing apparel attributes has recently drawn great interest in the computer vision community. Methods based on various deep neural networks have been proposed for image classification, which could be applied to apparel attributes recognition. An interesting problem raised is how to ensemble these methods to furthe…
A novel method for classification with rejection using ensemble of cost-sensitive classifiers.
Paper presents a Bayesian-decision-theory framework for long-tailed classification.
Enhances robustness for time series classification using self-ensemble method.
Ensembles of classifier models typically deliver superior performance and can outperform single classifier models given a dataset and classification task at hand. However, the gain in performance comes together with the lack in comprehensibility, posing a challenge to understand how each model affects the classificatio…
Despite the success of ensemble classification methods in multi-class classification problems, ensemble methods based on approaches other than bagging have not been widely explored for multi-label classification problems. The Kalman Filter-based Heuristic Ensemble (KFHE) is an ensemble method that exploits the sensor f…
This paper evaluates methods to improve classification on imbalanced datasets.
Revises Bayesian model averaging for foundation models.
RaSE ensemble framework improves sparse classification accuracy.
Quantum algorithm improves ensemble classification with reduced memory and time requirements.
It is common that a trained classification model is applied to the operating data that is deviated from the training data because of noise. This paper demonstrates that an ensemble classifier, Diversified Multiple Tree (DMT), is more robust in classifying noisy data than other widely used ensemble methods. DMT is teste…
Classifier ensembles are pattern recognition structures composed of a set of classification algorithms (members), organized in a parallel way, and a combination method with the aim of increasing the classification accuracy of a classification system. In this study, we investigate the application of a generalized mixtur…
Differentially private ensemble classifiers adapt to data streams while protecting privacy.
Improved ROCKET algorithm for brain activity classification.
Adaptive XGBoost improves accuracy on evolving data streams by updating the ensemble dynamically.
Deep neural networks have revolutionized many fields such as computer vision and natural language processing. Inspired by this recent success, deep learning started to show promising results for Time Series Classification (TSC). However, neural networks are still behind the state-of-the-art TSC algorithms, that are cur…
This work studies a unified approach to ensemble aggregation using likelihood perspective.
Temporal networks are ubiquitous and evolve over time by the addition, deletion, and changing of links, nodes, and attributes. Although many relational datasets contain temporal information, the majority of existing techniques in relational learning focus on static snapshots and ignore the temporal dynamics. We propose…
Diversity or complementarity of experts in ensemble pattern recognition and information processing systems is widely-observed by researchers to be crucial for achieving performance improvement upon fusion. Understanding this link between ensemble diversity and fusion performance is thus an important research question. …
HIVE-COTE v1.0 improves time series classification with enhanced usability.
Ensembling improves performance when classifiers disagree more than average.
Ensembles of models often yield improvements in system performance. These ensemble approaches have also been empirically shown to yield robust measures of uncertainty, and are capable of distinguishing between different \emph{forms} of uncertainty. However, ensembles come at a computational and memory cost which may be…
New ensemble models classify mouse movement trajectories to assess survey question difficulty.
Ensemble learning use multiple algorithms to obtain better predictive performance than any single one of its constituent algorithms could. With growing popularity of deep learning, researchers have started to ensemble them for various purposes. Few if any, however, has used the deep learning approach as a means to ense…
Automates zero-shot classification by scoring and weighting prompts.
Two proteins are homologous if they have a common evolutionary origin, and the binary classification problem is to identify proteins in a candidate set that are homologous to a particular native protein. The feature (explanatory) variables available for classification are various measures of similarity of proteins. The…
Removing or filtering outliers and mislabeled instances prior to training a learning algorithm has been shown to increase classification accuracy. A popular approach for handling outliers and mislabeled instances is to remove any instance that is misclassified by a learning algorithm. However, an examination of which l…
Novel analysis improves weighted majority vote in multiclass classification.