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.
Advances rule-based multi-label classification using conformal prediction.
Optimal classification rules control error rates in multiclass mixture models.
SigD2 reduces noisy rules in rule-based classifiers for better accuracy and readability.
New scoring rules improve probabilistic classification model evaluation.
Paper classifies ruled surfaces in Lorentz-Minkowski space for a specific flow.
Multi-label classification (MLC) is a supervised learning problem in which, contrary to standard multiclass classification, an instance can be associated with several class labels simultaneously. In this chapter, we advocate a rule-based approach to multi-label classification. Rule learning algorithms are often employe…
New rule-based method for classification with scalability, interpretability, and fairness.
This paper gives, in generic situations, a complete classification of ruled minimal surfaces in pseudo-Euclidean space with arbitrary index. In addition, we discuss the condition for ruled minimal surfaces to exist, and give a counter-example on the problem of Bernstein type.
SOAR generates rules for both positive and negative classes in binary classification.
A new classification rule for FDA improves classification performance by accounting for unequal covariance matrices.
Conventional techniques for supervised classification constrain the classification rules considered and use surrogate losses for classification 0-1 loss. Favored families of classification rules are those that enjoy parametric representations suitable for surrogate loss minimization, and low complexity properties suita…
Paper presents unsupervised calibration for split conformal classification.
We discuss theoretical aspects of the product rule for classification problems in supervised machine learning for the case of combining classifiers. We show that (1) the product rule arises from the MAP classifier supposing equivalent priors and conditional independence given a class; (2) under some conditions, the pro…
Paper extends transfer learning for decision rules, improving treatment rule estimation.
Interpretable classifiers have recently witnessed an increase in attention from the data mining community because they are inherently easier to understand and explain than their more complex counterparts. Examples of interpretable classification models include decision trees, rule sets, and rule lists. Learning such mo…
Study classifies ruled surfaces critical to Dirichlet energy.
New scoring rules compare probabilistic top lists in classification.
The paper classifies ruled surfaces in Lorentz-Minkowski space that are stationary for the moment of inertia.
Novel approach for creating interpretable classifiers using bilevel optimization of split-rules in NLDTs.
Simplifies random forests by breaking down trees into rules for better interpretability.
Paper proves fair classification can be done via simple thresholding.
A new bias score method optimizes fairness in classification.
pRSL combines probabilistic rules to improve multi-label classification.
We show that hyperelliptic symplectic Lefschetz fibrations are symplectically birational to two-fold covers of rational ruled surfaces, branched in a symplectically embedded surface. This reduces the classification of genus 2 fibrations to the classification of certain symplectic submanifolds in rational ruled surfaces…
This article presents GuideR, a user-guided rule induction algorithm, which overcomes the largest limitation of the existing methods-the lack of the possibility to introduce user's preferences or domain knowledge to the rule learning process. Automatic selection of attributes and attribute ranges often leads to the sit…
New learning rules achieve optimal sample complexity for weakly supervised classification.
Many leading classification algorithms output a classifier that is a weighted average of kernel evaluations. Optimizing these weights is a nontrivial problem that still attracts much research effort. Furthermore, explaining these methods to the uninitiated is a difficult task. Letting all the weights be equal leads to …
In this paper, using the classifications of timelike and spacelike ruled surfaces, we define and study the Mannheim offsets of spacelike ruled surfaces in Minkowski 3-space. We give the conditions for spacelike offset surfaces to be developable.
This paper deals with the binary classification task when the target class has the lower probability of occurrence. In such situation, it is not possible to build a powerful classifier by using standard methods such as logistic regression, classification tree, discriminant analysis, etc. To overcome this short-coming o…
In this paper, Legendre curves on unit tangent bundle are given using rotation minimizing (RM) vector fields. Ruled surfaces corresponding to these curves are represented. Singularities of these ruled surfaces are also analyzed and classifed.
A new ensemble method using random projections for kNN classification.
Critiques binary classification evaluation methods, advocating for proper scoring rules.
Unique inhomogeneous ruled hypersurface found in complex hyperbolic space.
Prototype rules simplify multiclass classification in metric spaces, achieving consistency and reduced complexity.
In this paper, using the classifications of timelike and spacelike ruled surfaces, we study the Mannheim offsets of timelike ruled surfaces in Minkowski 3-space. Firstly, we define the Mannheim offsets of a timelike ruled surface by considering the Lorentzian casual character of the offset surface. We obtain that the M…
New split rules improve subpopulation targeting in policy-making.
TransINT embeds KGs by preserving implication rules, outperforming existing methods.
A prediscretisation of numerical attributes which is required by some rule learning algorithms is a source of inefficiencies. This paper describes new rule tuning steps that aim to recover lost information in the discretisation and new pruning techniques that may further reduce the size of rule models and improve their…
This work bounds classification error in machine learning for low Bayes error conditions.
Subdivision rules create sequences of nested cell structures on CW-complexes, and they frequently arise from groups. In this paper, we develop several tools for classifying subdivision rules. We give a criterion for a subdivision rule to represent a Gromov hyperbolic space, and show that a subdivision rule for a hyperb…
Study ruled real hypersurfaces in nonflat complex space forms with constant norm shape operators.
New method improves model explainability and accuracy with low computational cost.
Classifies rank-one submanifolds in Euclidean space.
As a contribution to interpretable machine learning research, we develop a novel optimization framework for learning accurate and sparse two-level Boolean rules. We consider rules in both conjunctive normal form (AND-of-ORs) and disjunctive normal form (OR-of-ANDs). A principled objective function is proposed to trade …
This paper considers generalized linear models using rule-based features, also referred to as rule ensembles, for regression and probabilistic classification. Rules facilitate model interpretation while also capturing nonlinear dependences and interactions. Our problem formulation accordingly trades off rule set comple…
MRCs minimize worst-case expected 0-1 loss and provide performance guarantees.
Interpretability has always been a major concern for fuzzy rule-based classifiers. The usage of human-readable models allows them to explain the reasoning behind their predictions and decisions. However, when it comes to Big Data classification problems, fuzzy rule-based classifiers have not been able to maintain the g…
We show that ruled real hypersurfaces with constant mean curvature in the complex projective and hyperbolic spaces must be minimal. This provides their classification, by virtue of a result of Lohnherr and Reckziegel.