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.
Study adversarial attacks on cost-sensitive classifiers.
Proposes an angle-based framework for multicategory cost-sensitive classification.
In binary classification framework, we are interested in making cost sensitive label predictions in the presence of uniform/symmetric label noise. We first observe that - Bayes classifiers are not (uniform) noise robust in cost sensitive setting. To circumvent this impossibility result, we present two schemes; un…
In this paper, we propose an effective THresholding method based on ORder Statistic, called THORS, to convert an arbitrary scoring-type classifier, which can induce a continuous cumulative distribution function of the score, into a cost-sensitive one. The procedure, uses order statistic to find an optimal threshold for…
Several recent works have developed methods for training classifiers that are certifiably robust against norm-bounded adversarial perturbations. These methods assume that all the adversarial transformations are equally important, which is seldom the case in real-world applications. We advocate for cost-sensitive robust…
A novel method for classification with rejection using ensemble of cost-sensitive classifiers.
A new procedure for learning cost-sensitive SVM(CS-SVM) classifiers is proposed. The SVM hinge loss is extended to the cost sensitive setting, and the CS-SVM is derived as the minimizer of the associated risk. The extension of the hinge loss draws on recent connections between risk minimization and probability elicitat…
Combines cost-sensitive and Neyman-Pearson paradigms for better binary classification.
New algorithm reduces misclassification costs in neural networks.
A new approach to cost-sensitive multiclass classification prioritizes certain classes over others.
Recently, machine learning algorithms have successfully entered large-scale real-world industrial applications (e.g. search engines and email spam filters). Here, the CPU cost during test time must be budgeted and accounted for. In this paper, we address the challenge of balancing the test-time cost and the classifier …
Training classification models on imbalanced data tends to result in bias towards the majority class. In this paper, we demonstrate how variable discretization and cost-sensitive logistic regression help mitigate this bias on an imbalanced credit scoring dataset, and further show the application of the variable discret…
Extends expected value framework for cost-sensitive causal decision-making.
Paper proposes consistent estimators for learning to defer decisions to experts.
Improves classifier evaluation by aligning with Total Classification Cost.
The paper explores fair regression and classification under demographic parity constraints.
Enhances Random Forest for imbalanced functional data classification.
This paper proposes CSADA to make DNNs cost-sensitive.
New algorithm boosts classification for imbalanced data.
Environmental acoustic sensing involves the retrieval and processing of audio signals to better understand our surroundings. While large-scale acoustic data make manual analysis infeasible, they provide a suitable playground for machine learning approaches. Most existing machine learning techniques developed for enviro…
Unified framework for cost-sensitive ensemble learning.
Boosting theory extended to handle cost-sensitive and multi-objective losses.
Unified method for learning from selectively labeled data.
While both cost-sensitive learning and online learning have been studied extensively, the effort in simultaneously dealing with these two issues is limited. Aiming at this challenge task, a novel learning framework is proposed in this paper. The key idea is based on the fusion of online ensemble algorithms and the stat…
Paper proposes AdaBoost-assisted ELM for efficient online sequential classification.
Label embedding (LE) is an important family of multi-label classification algorithms that digest the label information jointly for better performance. Different real-world applications evaluate performance by different cost functions of interest. Current LE algorithms often aim to optimize one specific cost function, b…
Optimized deferral improves accuracy in imbalanced settings.
This study integrates cost-sensitive and causal classification methods.
Cost-Sensitive Online Classification has drawn extensive attention in recent years, where the main approach is to directly online optimize two well-known cost-sensitive metrics: (i) weighted sum of sensitivity and specificity; (ii) weighted misclassification cost. However, previous existing methods only considered firs…
A deep reinforcement learning method for cost-sensitive portfolio selection.
Study improves top-k set prediction with low cardinality.
Label space expansion for multi-label classification (MLC) is a methodology that encodes the original label vectors to higher dimensional codes before training and decodes the predicted codes back to the label vectors during testing. The methodology has been demonstrated to improve the performance of MLC algorithms whe…
This paper tackles cost-sensitive portfolio optimization under ambiguous return distributions.
We study consistency of learning algorithms for a multi-class performance metric that is a non-decomposable function of the confusion matrix of a classifier and cannot be expressed as a sum of losses on individual data points; examples of such performance metrics include the macro F-measure popular in information retri…
New algorithms optimize metrics for binary classification with class imbalance.
We study the approximate nearest neighbour method for cost-sensitive classification on low-dimensional manifolds embedded within a high-dimensional feature space. We determine the minimax learning rates for distributions on a smooth manifold, in a cost-sensitive setting. This generalises a classic result of Audibert an…
Bayesian approach detects changepoints with cost-sensitive data fidelity.
Depression is one of the most common mental health disorders, and a large number of depressed people commit suicide each year. Potential depression sufferers usually do not consult psychological doctors because they feel ashamed or are unaware of any depression, which may result in severe delay of diagnosis and treatme…
Abstaining classifiers have been widely used in cost-sensitive applications to avoid ambiguous classification and reduce the cost of misclassification. Previous abstaining classification models rely on cost information, such as a cost matrix or cost ratio. However, it is difficult to obtain or estimate costs in practic…
This paper proposes a cost-sensitive feature acquisition method.
The cost-sensitive classification problem plays a crucial role in mission-critical machine learning applications, and differs with traditional classification by taking the misclassification costs into consideration. Although being studied extensively in the literature, the fundamental limits of this problem are still n…
Survey on assessing and improving classifier calibration for better decision making.
Proposes WPGD for cost-sensitive adversarial training in deep learning.
Paper proposes a cost-sensitive conformal training method with provably controllable learning bounds.
Imbalanced data with a skewed class distribution are common in many real-world applications. Deep Belief Network (DBN) is a machine learning technique that is effective in classification tasks. However, conventional DBN does not work well for imbalanced data classification because it assumes equal costs for each class.…
Improves generative models for cost-sensitive decisions.
A new method for decision-focused learning reduces computational cost.
Proposes cost-sensitive feature selection for SVMs.