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Computational Intelligence is a peer-reviewed journal published by Wiley, covering Logic, Reasoning, and Knowledge, AI-based Problem Solving and Planning, Semantic Web and Ontologies.
| Publisher | Wiley |
|---|---|
| Reference style | Author–year (Chicago) Author–year — (Smith, 2023) in the text Smith, Ada, Ben Jones, and Cara Lee. 2023. "A Representative Article Title." Computational Intelligence 12 (3): 45–58.
Formats any DOI in Computational Intelligence style. No sign-up. |
| Publishes research in | Logic, Reasoning, and Knowledge AI-based Problem Solving and Planning Semantic Web and Ontologies Natural Language Processing Techniques Topic Modeling |
| ISSN | 0824-7935 |
| Citation impact (2-yr) | 1.57 |
| h-index | 89 |
| i10-index | 689 |
| Total citations | 43,609 |
| Article processing charge | $3,450 |
| Top institutions publishing here | University of Waterloo |
| Journal website | onlinelibrary.wiley.com |
| You get | A submission-ready PDF and the editable LaTeX source — ready to submit. |
Citations each year’s papers have accumulated so far — the most recent years are still building up.
Even though considerable attention has been given to the polarity of words (positive and negative) and the creation of large polarity lexicons, research in emotion analysis has had to rely on limited and small emotion lexicons. In this paper, we show how the combined strength and wisdom of the crowds can be used to generate…
An architecture for a rational agent must allow for means‐end reasoning, for the weighing of competing alternatives, and for interactions betwen these two forms of reasoning. Such an architecture must also address the problem of resource boundedness. We sketch a solution of the first problem that points the way to a solution of the second.…
Resampling methods are commonly used for dealing with the class‐imbalance problem. Their advantage over other methods is that they are external and thus, easily transportable. Although such approaches can be very simple to implement, tuning them most effectively is not an easy task. In particular, it is unclear whether oversampling is more effective than undersampling…
Reasoning about change requires predicting how long a proposition, having become true, will continue to be so. Lacking perfect knowledge, an agent may be constrained to believe that a proposition persists indefinitely simply because there is no way for the agent to infer a contravening proposition with certainty. In this paper, we describe a model…
A new approach for learning Bayesian belief networks from raw data is presented. The approach is based on Rissanen's minimal description length (MDL) principle, which is particularly well suited for this task. Our approach does not require any prior assumptions about the distribution being learned. In particular, our method can learn unrestricted multiply‐connected belief networks.…
