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arXiv research

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.

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6 results for “informedness”

Evaluation: from precision, recall and F-measure to ROC, informedness, markedness and correlationcs.LG

The paper critiques and expands on common evaluation metrics in machine learning.

problem The common evaluation metrics like Precision, Recall, F-Measure, and Rand Accuracy are biased and misleading.

method The paper introduces new measures like Informedness, Markedness, and Correlation to better reflect the quality of predictions.

result A system that performs worse in terms of Informedness can appear better using common measures like Precision and Recall.

evaluation metrics machine learning informedness markedness correlation

2020-10-11abs ↗pdf ↗

Market Informedness and Market-Maker Profitability: The Trade-Off Between Adverse Selection and Price Discoveryq-fin.TR

Study shows informed traders harm market makers but price discovery benefits outweigh costs.

problem Informed traders' impact on market makers' profitability.

method Agent-based model with heterogeneous learning agents, multi-agent reinforcement learning.

result Informed market order flow is harmful when aggregate informedness is low but beneficial as it increases.

market informedness market makers adverse selection price discovery heterogeneous learning

2026-06-04abs ↗pdf ↗

INDEQS: Informed Neural controlled Differential EQuationScs.LG

INDEQS: A Graph-Based Neural Controlled Differential Equation Framework for Forecasting

problem Forecasting time series with neural networks

method Incorporating prior knowledge of a directed graph

result Outer informedness consistently improves forecasting accuracy

forecasting neural networks graph informedness differential equations

2026-06-17abs ↗pdf ↗

ADABOOK & MULTIBOOK: Adaptive Boosting with Chance Correctioncs.LG

New algorithms improve boosting by optimizing chance-corrected measures.

problem Improving boosting algorithms to use chance-corrected measures effectively.

method Developed new algorithms (AdaBook and Multibook) that optimize chance-corrected measures.

result AdaBook and Multibook outperform standard Multiboost or AdaBoost in multiclass situations.

boosting AdaBoost Multiboost chance-corrected Kappa

2020-10-11abs ↗pdf ↗

Evaluation Evaluation a Monte Carlo studycs.AI

Over the last decade there has been increasing concern about the biases embodied in traditional evaluation methods for Natural Language Processing/Learning, particularly methods borrowed from Information Retrieval. Without knowledge of the Bias and Prevalence of the contingency being tested, or equivalently the expecta…

2015-04-03abs ↗pdf ↗

Credal Self-Supervised Learningstat.ML

Improved self-supervised learning using credal sets.

problem Lack of precise knowledge in pseudo-labels.

method Using credal sets (sets of probability distributions) for labeling unlabeled data.

result Competitive to superior performance in low-label scenarios.

self-supervised learning credal sets superset learning semi-supervised learning weakly labeled data

2021-06-22abs ↗pdf ↗