arXiv research

The paper analyzes the benefit-cost ratio for feature selection in machine learning.

This paper proposes a cost-sensitive feature acquisition method.

Paper proposes using pairwise feature comparisons to infer modification costs for user recourse.

Cost-effective feature selection improves network model choice.

Cost-efficient feature selection for multi-label classification in medicine.

Paper tackles cost-sensitive diagnosis and learning in healthcare, assigning feature costs based on patient discomfort.

We seek decision rules for prediction-time cost reduction, where complete data is available for training, but during prediction-time, each feature can only be acquired for an additional cost. We propose a novel random forest algorithm to minimize prediction error for a user-specified {\it average} feature acquisition b…

A new feature selection method for cost-sensitive classification in Random Forests.

VFDS selects dynamic features for efficient HAR tasks, optimizing performance-cost trade-offs.

In real-world scenarios, different features have different acquisition costs at test-time which necessitates cost-aware methods to optimize the cost and performance trade-off. This paper introduces a novel and scalable approach for cost-aware feature acquisition at test-time. The method incrementally asks for features …

Proposes cost-sensitive feature selection for SVMs.

We consider the problem of learning decision rules for prediction with feature budget constraint. In particular, we are interested in pruning an ensemble of decision trees to reduce expected feature cost while maintaining high prediction accuracy for any test example. We propose a novel 0-1 integer program formulation …

Investigates the cost-effectiveness of security features in smart card chips.

AFA evaluates AI feature acquisition strategies in domains with high costs.

In many real-world learning scenarios, features are only acquirable at a cost constrained under a budget. In this paper, we propose a novel approach for cost-sensitive feature acquisition at the prediction-time. The suggested method acquires features incrementally based on a context-aware feature-value function. We for…

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 …

One-shot algorithm for feature-distributed kernel PCA reduces communication costs.

This paper introduces a family of local feature aggregation functions and a novel method to estimate their parameters, such that they generate optimal representations for classification (or any task that can be expressed as a cost function minimization problem). To achieve that, we compose the local feature aggregation…

Designs a neural network to reduce training cost by mapping to higher dimensions.

Sparse nearest-centroid classifiers detect relevant features for classification.

This paper uses Reinforcement Learning to select features from a large dataset.

A fast method estimates stability of ensemble feature selectors.

Feature missing is a serious problem in many applications, which may lead to low quality of training data and further significantly degrade the learning performance. While feature acquisition usually involves special devices or complex process, it is expensive to acquire all feature values for the whole dataset. On the…

Optimizes biomarker selection for cost-effective treatment rules.

Integrates Fourier features for faster Gaussian process regression.

Frequently, acquiring training data has an associated cost. We consider the situation where the learner may purchase data during training, subject TO a budget. IN particular, we examine the CASE WHERE each feature label has an associated cost, AND the total cost OF ALL feature labels acquired during training must NOT e…

New framework reduces strategic manipulation cost for minority groups in fair classification.

Cost-effective models detect depression from speech.

Simplifies decision-making during medical exams with cost-efficient feature acquisition.

New features reduce computational cost of variational inference.

A faster graph kernel using optical random features.

Two new methods reduce communication costs in federated learning.

We consider the problem of active feature acquisition, where we sequentially select the subset of features in order to achieve the maximum prediction performance in the most cost-effective way. In this work, we formulate this active feature acquisition problem as a reinforcement learning problem, and provide a novel fr…

A deep reinforcement learning method for cost-sensitive portfolio selection.

Kernel improves Gaussian process scalability for wide datasets.

Paper proposes new costs for learning multiple centers in MDNs.

Paper develops a new kernel expansion method using entropic optimal features for sparse and efficient kernel approximation.

A new method reduces feature screening cost from $O(np)$ to $O(\sqrt{n}p)$.

IAL uses interactive learning to improve model performance with minimal human feedback.

The paper develops a method to learn robust decision policies from observational data, reducing high-cost outcomes.

DFR reduces the computational cost of sparse-group lasso and adaptive sparse-group lasso.

We study black-box attacks on machine learning classifiers where each query to the model incurs some cost or risk of detection to the adversary. We focus explicitly on minimizing the number of queries as a major objective. Specifically, we consider the problem of attacking machine learning classifiers subject to a budg…

New pricing framework allocates costs of operating reserves and transmission.

As machine learning algorithms enter applications in industrial settings, there is increased interest in controlling their cpu-time during testing. The cpu-time consists of the running time of the algorithm and the extraction time of the features. The latter can vary drastically when the feature set is diverse. In this…

Optimizing post-crisis recovery in scale-free networks by stimulating high-degree nodes.

Resource-efficient oblique trees reduce neural signal classification costs.

Efficient algorithms for clustered Lasso and OSCAR reduce computational costs.

With the advent of Big Data era, data reduction methods are highly demanded given its ability to simplify huge data, and ease complex learning processes. Concretely, algorithms that are able to filter relevant dimensions from a set of millions are of huge importance. Although effective, these techniques suffer from the…