Review of LVQ classifiers, proposing a taxonomy and comparing eleven approaches.
problem Improving LVQ classifiers through taxonomy and comparison.
method Taxonomy integration, comparison of eleven LVQ classifiers.
result Comparison of eleven LVQ classifiers on real-world and artificial datasets.
A framework for prototype-based classifiers in changing data environments.
problem Learning in non-stationary environments with concept drift.
method Analytical methods from statistical physics applied to LVQ systems.
result Basic LVQ algorithms are suitable for non-stationary environments, but weight decay does not improve performance.
Efficiently computes counterfactual explanations for LVQ models.
problem Need to efficiently explain predictions of machine learning models.
method Derives convex and non-convex programs for LVQ models.
result Efficient computation of counterfactual explanations for LVQ models.
LVQ models robustness evaluated against adversarial attacks.
problem Robustness of LVQ models against adversarial attacks.
method Evaluation of three LVQ models: Generalized LVQ, Generalized Matrix LVQ, and Generalized Tangent LVQ.
result Generalized LVQ and Generalized Tangent LVQ are robust, while Generalized Matrix LVQ is not.
Improved nearest neighbor classification for time series data.
problem High storage and computation requirements for large training sets in DTW space.
method Extends LVQ to DTW spaces using asymmetric weighted averaging.
result Asymmetric GLVQ outperforms other methods in nearest neighbor classification.
A new method simplifies credit scoring by reducing rules from complex data.
problem Time-consuming and difficult classification of customer profiles for credit risk.
method Combines LVQ neural network with PSO optimization for reduced rules.
result Very satisfactory results in credit consumer financial institution database.
We propose in this contribution a method for l one regularization in prototype based relevance learning vector quantization (LVQ) for sparse relevance profiles. Sparse relevance profiles in hyperspectral data analysis fade down those spectral bands which are not necessary for classification. In particular, we consider …
Fast online Kernel SVM for big data with limited resources.
problem Efficiently training SVM on large datasets with limited computational resources.
method Split input space using LVQ, train SVM in clusters, limit support vectors.
result Achieves high accuracy with high throughput on large datasets.
Framework models supervised learning in non-stationary data.
problem Non-stationary data in supervised learning.
method Statistical physics methods applied to LVQ and neural networks.
result LVQ and ReLU have different sensitivity to concept drift.
In this paper we propose a simple yet powerful method for learning representations in supervised learning scenarios where each original input datapoint is described by a set of vectors and their associated outputs may be given by soft labels indicating, for example, class probabilities. We represent an input datapoint …
Supervised statistical classification is a vital tool for satellite image processing. It is useful not only when a discrete result, such as feature extraction or surface type, is required, but also for continuum retrievals by dividing the quantity of interest into discrete ranges. Because of the high resolution of mode…
Bayesian network classifiers are used in many fields, and one common class of classifiers are naive Bayes classifiers. In this paper, we introduce an approach for reasoning about Bayesian network classifiers in which we explicitly convert them into Ordered Decision Diagrams (ODDs), which are then used to reason about t…
A new trust score improves classifier reliability.
problem Improving classifier reliability in safety-critical applications.
method Proposed a trust score based on classifier agreement with a modified nearest-neighbor classifier.
result Empirically shows high trust scores produce high precision in identifying correctly classified examples.
Study on the structure of classifier boundaries in DNA sequencing.
problem Understanding the structure of boundaries in a Bayes classifier for DNA sequencing.
method Examined the structure of the boundary in a Bayes classifier applied to DNA sequencing data. Introduced a new measure of uncertainty, Neighbor Similarity.
result The boundary is large and complex, and Neighbor Similarity effectively measures classifier uncertainty.
RCAM-based ensemble combines binary classifiers using similarity and vote scheme.
problem Improving binary classification accuracy through ensemble methods.
method RCAM-based ensemble combining classifiers using similarity and recurrent consult-vote scheme.
result RCAM-based ensemble outperforms individual classifiers and majority voting.
Proposes SDWEC for improved classification accuracy and reduced classifier count.
problem Improves classification accuracy and minimizes the number of classifiers.
method Formed an ensemble of pre-trained classifiers with assigned weights, modeled as a non-convex cost function with terms for data fidelity, sparsity, and non-negativity constraints. Employed convex relaxation techniques and approximations for efficient solution.
result SDWEC provides better or similar accuracy levels using fewer classifiers and reduces testing time.
Random classifier ensembles improve fairness and diversity in decision making.
problem Loss of diversity and fairness in single machine learning classifiers.
method Study of random classifier ensembles for fairness-aware learning.
result Ensemble of classifiers can achieve better fairness and accuracy trade-offs.
Unsupervised classification methods learn a discriminative classifier from unlabeled data, which has been proven to be an effective way of simultaneously clustering the data and training a classifier from the data. Various unsupervised classification methods obtain appealing results by the classifiers learned in an uns…
Boosting classifiers improve accuracy with noisy inputs.
problem Noisy communication or computation degrades boosting classifier accuracy.
method Optimize resource allocation for base classifiers based on importance metrics.
result Optimized noisy boosting classifiers are more robust than bagging.
ECPF improves classification accuracy and speed for evolving data streams.
problem Reusing classifiers trained on recurring concepts to maintain accuracy and speed.
method ECPF uses similarity of classifications to quickly identify the best classifier to reuse.
result ECPF significantly outperforms state-of-the-art frameworks on synthetic and real-world datasets.
New method computes discriminative classifiers from generative models.
problem Discriminative vs generative classifiers are often seen as distinct, but this work shows they can be equivalent.
method General theoretical result showing generative classifiers can be computed discriminatively.
result Bayesian Maximum Posterior classifier from generative models matches discriminative classifier definition.
Generative classifier derived from any discriminative classifier rejects illegal inputs.
problem Detecting and rejecting illegal inputs like adversarial examples and out-of-distribution samples.
method SDIM-logit: learns generative classifier from logits of any discriminative classifier, imposing statistical constraints.
result SDIM-logit inherits performance of base classifier without loss and can reject illegal inputs.
Simple construction for classifying spaces of projections of immersions.
problem Classifying spaces for projections of immersions with controlled singularities.
method Explicit simple construction for classifying spaces of maps obtained as hyperplane projections of immersions.
result Structure theorems for these classifying spaces.
Study examines how classifier performance is affected by training data quality.
problem How classifier performance is affected by training data quality.
method Extensive numerical experiments with four classifiers (Bayes, neural nets, partition models, random forests) on metagenomic assembly data.
result Classifier performance degrades as training data quality degrades, leading to breakdown-like behavior.
Improves multi-label classification with a new network model.
problem Improving multi-label classification accuracy.
method Introduces Classifier Chain Network (CCN) for multi-label classification.
result CCN outperforms benchmark methods in simulations and real data.
FOCA method prevents co-adaptation between feature extractor and classifier.
problem Co-adaptation between feature extractor and classifier degrades neural network performance.
method FOCA method uses randomly-generated, weak classifiers to optimize feature extractor without explicit co-adaptation.
result FOCA features form a point-like distribution within the same class under special conditions.
A novel KD classifier improves character recognition performance.
problem Improving character recognition accuracy.
method Kernel-based generative classifier in distortion subspace with iterative kernel selection.
result The KD classifier outperforms existing classifiers and has unique recognition capability.
A review of statistical SSL methods showing improved classifier performance.
problem Forming classifiers from limited labeled data and many unlabeled data.
method Statistical approaches to semi-supervised learning.
result A classifier from partially labeled data can have lower expected error rate.
Paper proposes a classifier that optimizes utility function with prior knowledge.
problem Designing a classifier that optimizes a utility function based on prior knowledge.
method Systematic framework incorporating prior knowledge to optimize a utility function.
result The classifier asymptotically converges to the optimal classifier (Bayes rule) as data size grows.
A bias classifier is introduced to resist adversarial attacks.
problem Resisting adversarial attacks on deep neural networks (DNNs).
method Introducing the bias part of a DNN with Relu as the activation function as a classifier, and adding a random first-degree part to make it information-theoretically safe.
result The bias classifier is more robust than DNNs of similar size against adversarial attacks.
Deep Bayes classifiers are more robust to adversarial attacks than discriminative classifiers.
problem Robustness of deep neural network classifiers against adversarial attacks.
method Developed deep Bayes classifier using conditional deep generative models and detection methods.
result Deep Bayes classifiers are more robust than deep discriminative classifiers.
New method controls classifier guidance in diffusion models.
problem Improving classifier guidance in diffusion models.
method Cross-entropy control of classifier gradients.
result Effective guidance vectors with mean squared error O(dε). META-DES framework selects best classifiers dynamically.
problem Dynamic selection of ensemble classifiers.
method Meta-learning approach to estimate classifier competence.
result META-DES framework improves classification performance.
A new classifier combines multiple data models.
problem Classifying functional data accurately.
method Nonlinear aggregation of multiple classifiers.
result The aggregation rule performs as well as the best classifier.
Paper introduces algorithms for explaining monotonic classifiers.
problem Need for explanations of monotonic classifiers.
method Polynomial algorithms for formal explanations of monotonic classifiers.
result Efficient model-agnostic algorithm for enumerating explanations.
This paper evaluates criteria for a versatile classifier.
problem Choosing an optimal classifier for all problems.
method Data analysis and scoring of six popular classifiers.
result Random forests are the best generalist classifiers.
Generative classifiers' properties are linked to linear constraints.
problem Understanding the Markov property in generative classifiers.
method Characterization of discrimination functions using linear constraints and a second order finite difference operator.
result Discrimination functions of undirected Markov network classifiers are characterized by sets of linear constraints.
Ensemble validation shows selectivity penalties but variety benefits.
problem Selecting classifiers for ensemble models and their error bounds.
method Forming an ensemble from a set of hypothesis classifiers, selecting randomly, with an error bound formula.
result No penalty for using a richer hypothesis set if same fraction selected.
Ensemble quantile classifier improves performance on high-dimensional data.
problem Discriminating high-dimensional data with heavy-tailed or skewed inputs.
method Regularized quantile classifier that assigns variable weights.
result Consistently estimates minimal population loss and is Bayes optimal.
In this paper we present a new Bayesian network model for classification that combines the naive-Bayes (NB) classifier and the finite-mixture (FM) classifier. The resulting classifier aims at relaxing the strong assumptions on which the two component models are based, in an attempt to improve on their classification pe…
Generative text classifiers are most vulnerable to membership inference attacks.
problem Privacy threat from Membership Inference Attacks on generative text classifiers.
method Comprehensive empirical evaluation of generative, discriminative, and pseudo-generative classifiers across various datasets.
result Generative classifiers explicitly modeling P(X,Y) are most vulnerable to membership leakage. New uniqueness concept for adversarial Bayes classifier.
problem Understanding adversarial Bayes classifiers in binary classification.
method Developed a new notion of uniqueness and analyzed it for a family of one-dimensional data distributions.
result Improved regularity of adversarial Bayes classifiers as perturbation radius increases.
A new method combines classifiers using possibility distributions and adaptive t-norms.
problem Aggregating predictions from multiple classifiers trained on overlapping datasets.
method Proposes a new approach to aggregate classifier predictions using possibility theory and adaptive t-norms.
result Proves the proposed approach possesses desirable robustness properties.
META-DES.H selects competent classifiers using meta-learning and dynamic weighting.
problem Selecting competent classifiers in dynamic ensemble selection.
method META-DES framework using meta-learning and dynamic weighting.
result Improvements in recognition accuracy on 30 datasets.
Two strategies for training network classifiers with feature heterogeneity.
problem Training network classifiers with agents having varying feature sizes and unreliable local decisions.
method Promotes global and local smoothing of classifier outputs.
result Output smoothing makes network classifier dynamics more complex, requiring regularization of parameters.
A new classifier for high-dimensional data with spiked eigenvalues.
problem Classifying high-dimensional data with spiked eigenvalues.
method Distance-based classifier using data transformation and noise reduction.
result The new classifier performs better than existing methods on simulated and real data.
Two constructions of classifying spaces for singular maps are connected.
problem Understanding the classifying spaces of singular maps.
method Homotopy theoretical connection between two constructions.
result Some classifying spaces have a simple product structure.
Binary linear classifiers are the most explainable up to negligible sets.
problem Measuring the explainability of machine learning classifiers.
method Introducing pointwise coverage to measure explainability and proving the binary linear classifier is the most explainable up to negligible sets.
result The binary linear classifier is uniquely the most explainable classifier up to negligible sets.