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Mutual information and sensitivity analysis for feature selection in customer targeting: a comparative study — arXiv:2608.20447

Source: arXiv (en)
Original: https://arxiv.org/abs/2608.20447
License (as archived): CC0-1.0
Archived: 2026-10-18T10:31:01.199Z
SHA-256 of the archived text: e37b281a999f483663fe85df805092b492902b1b295249ce40217e5d6f963123
Stable URL of this page: https://metraipolis.com/documento/dc_f15445bc37da

How to cite

Mutual information and sensitivity analysis for feature selection in customer targeting: a comparative study — arXiv:2608.20447 — arXiv (en), https://arxiv.org/abs/2608.20447 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_f15445bc37da (Metraipolis archive dc_f15445bc37da, sha256 e37b281a999f483663fe85df805092b492902b1b295249ce40217e5d6f963123)

BibTeX

@misc{dcf15445bc37da,
  title = {Mutual information and sensitivity analysis for feature selection in customer targeting: a comparative study — arXiv:2608.20447},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2608.20447},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_f15445bc37da; sha256 e37b281a999f483663fe85df805092b492902b1b295249ce40217e5d6f963123},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2608.20447. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

Feature selection is a highly relevant task in a data-driven knowledge discovery project. Several techniques have been developed aiming at finding the features that influence most an outcome to predict, including mutual information and, in recent years, the data-based sensitivity analysis. The present research focus on analyzing the advantages and disadvantages of each of these two techniques, by applying both to a bank telemarketing case. Thereafter, a logistic regression model is built on the tuned set of features identified by each of the two techniques as the most influencing set of features on the success of a telemarketing contact, in a total of 13 features for mutual information and 9 features for the data-based sensitivity analysis. The latter performs better for lower values of false positives while the former is slightly better for a higher false positive ratio. Thus, mutual information becomes a better choice if bank managers intend to reduce slightly the cost of contacts without risking losing a high number of successes. Such results show that mutual information, although not recent, is still a valid method for feature selection. On the other side, the data-based sensitivity analysis selection achieved good prediction results with less features.

Tema degli esploratori: agenti e intelligenza artificiale (retro-catalogo arXiv: annunci del 2026-08-24 in cs.LG).
Categorie arXiv: Machine Learning (cs.LG) ; Probability (math.PR).
Pagina del lavoro: https://arxiv.org/abs/2608.20447 — autori e testo completo sono lì (qui omessi i nomi: nessun dato personale nel deposito).
Licenza: titolo e abstract sono metadati arXiv distribuiti in CC0 1.0; il testo completo ha la licenza scelta dagli autori, indicata sulla pagina.
Letto il 2026-10-18T10:27:55.923Z dalla pagina https://arxiv.org/catchup/cs.LG/2026-08-24?abs=True (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).