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Source arXiv Machine Learning export.arxiv.org Analyst
August 24, 2026 research research

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

Presents methodological comparison without overt promotion, but omits implementation details, dataset provenance, and statistical rigor markers.

View original on arxiv.org

Overview

A comparative study evaluates mutual information and data-based sensitivity analysis for feature selection in bank telemarketing, finding mutual information selects 13 features with slightly better performance at high false positive ratios, while sensitivity analysis selects 9 features and achieves lower false positives.

TL;DR

  • Compares two feature selection methods on real banking telemarketing data
  • Mutual information yields 13 features; sensitivity analysis yields 9
  • Trade-off observed: mutual information favors cost reduction with modest success loss, sensitivity analysis favors precision at lower feature count

Key Stats

13

features selected by mutual information

From bank telemarketing dataset

9

features selected by sensitivity analysis

Same dataset, same prediction task

Questions Answered

What happened?Who is involved?Why does this matter?

Keywords

feature selectionmutual informationsensitivity analysistelemarketinglogistic regression

Narrative Frame

neutral_comparison_framing

The Fog

Spin Score

20%

Emphasizes practical interpretability of trade-offs; minimizes transparency around reproducibility, validation robustness, and external validity.

What the story wants you to believe

That mutual information remains practically viable for business-critical targeting tasks, and that data-based sensitivity analysis offers a credible, parsimonious alternative — both warranting consideration in applied settings.

What it makes harder to question

The sufficiency of qualitative performance reporting without statistical validation or reproducibility scaffolding.

How the spin works

Combines domain anchoring ('bank telemarketing') and outcome framing ('cost of contacts', 'success of contact') to lend practical weight, while omitting statistical safeguards and implementation specifics — creating an impression of actionable insight despite thin empirical validation.

Who Benefits If This Frame Spreads

  • Research authors

    Citation accrual in applied ML and feature selection literature

    The paper positions itself as a pragmatic, use-case-anchored comparison — a citation-friendly reference for practitioners weighing classical vs. emerging techniques.

The Frame

Methodologically cautious academic contribution

Missing Context

  • Dataset source and licensing
  • Number of samples and class distribution
  • Reproducibility instructions or code availability
  • Statistical significance of performance differences

Spin Types

Every story gets a Spin Verdict: a primary spin type (and secondary when the framing blends), a specific tactic name, and a score for how strongly the narrative is steered. Examples beneath each type are tactics, not separate categories.

The Cushion

— Softens negative news

Reframes setbacks, layoffs, delays, losses, or criticism as necessary transitions, efficiency moves, temporary headwinds, or strategic resets — making the downside feel smaller, more acceptable, or less alarming.

Tactics: job-loss softening · restructuring framing · efficiency framing · strategic reset · temporary headwinds

The Shield

— Deflects blame

Shifts responsibility away from the actor — toward regulators, market forces, competitors, bad actors, legacy systems, or abstract risks — while positioning the subject as reactive, responsible, or protective.

Tactics: regulatory blame shift · macroeconomic headwinds · safety framing · bad-actor framing · market-pressure framing

The Hype

— Amplifies future upside

Emphasizes breakthrough potential, massive growth, democratization, transformation, or category disruption while downplaying uncertainty, cost, adoption risk, or timeline friction.

Tactics: innovation framing · democratization · breakthrough framing · category creation · moonshot framing

The Halo

— Associates with virtue

Wraps the story in public-good language — responsibility, safety, inclusion, access, sustainability, national interest, or mission — so the subject appears morally aligned and criticism feels harder to make.

Tactics: altruistic reframing · public good · responsible AI framing · inclusion framing · mission-first framing

The Fog

— Obscures details primary

Uses jargon, passive voice, vague claims, complex phrasing, or missing specifics to make it harder to identify who decided what, what changed, what failed, or what trade-offs were made.

Tactics: strategic ambiguity · jargon saturation · passive voice distancing · accountability blur · undefined metrics

The Stampede

— Creates inevitability

Frames a trend, product, market shift, or decision as already happening, unavoidable, or something everyone must respond to now — creating urgency, FOMO, and pressure to accept the narrative.

Tactics: arms-race framing · inevitability framing · FOMO framing · adoption momentum · future-is-here framing

Spin Score measures how strongly the framing steers the narrative (0–100%). Higher scores mean more deliberate spin tactics — loaded language, selective emphasis, or omitted context. Many stories blend two types (e.g. Halo + Hype).

SpinGraph

How this belief gets built

Claim → Frame → Beneficiary → Gap → AI Risk

It presents two technical approaches as equally legitimate options for a real-world problem, using observed behavior rather than rigorous metrics to justify their utility — making methodological choice feel grounded and low-risk.

  1. Claim

    The data-based sensitivity analysis selection achieved good prediction results

    The data-based sensitivity analysis selection achieved good prediction results with less features.

  2. Frame

    Key details stay obscured

    Methodologically cautious academic contribution

  3. Beneficiary

    Citation accrual in applied ML and feature selection literature

    Research authors — Citation accrual in applied ML and feature selection literature

  4. Gap

    Dataset source and licensing

  5. AI Risk

    AI may repeat the headline as fact

    A study found mutual information selects more features but performs better when false positives are acceptable, while sensitivity analysis uses fewer features and reduces false positives.

Claim Ledger

01 Primary Technical Claim Present in Source risk:Low

The data-based sensitivity analysis selection achieved good prediction results with less features.

evidence: Qualitative directional comparison of false positive behavior under unspecified thresholds

"The latter performs better for lower values of false positives while the former is slightly better for a higher false positive ratio."

Evidence Gaps

  • Quantitative performance metrics (e.g., accuracy, F1, AUC)
  • Standard error or confidence intervals
  • Baseline model performance without feature selection

Fact Check Signals

No direct fact-check match found

0 of 1 claim matched · confidence: low · checked August 24, 2026

01 No direct match

The data-based sensitivity analysis selection achieved good prediction results with less features.

Fact Check Signals

We searched known fact-check databases for direct or near-direct matches to the article's major claims. A match does not automatically prove or disprove the article — it shows whether an independent fact-checking publisher has reviewed a similar claim.

  • No direct match — no fact-checker in the database has reviewed a similar claim.
  • Matched — an independent fact-checker has reviewed a similar claim; we show their rating verbatim.
  • Conflicting coverage — fact-checkers disagree on a similar claim.

This is evidence discovery, not an automated truth score. Ratings and wording come directly from the publishing fact-checker.

Frame Strength

Frame Strength

Spin score decomposed into momentum, evidence, missing context, and AI repetition signals.

Spin Score 20%
Evidence Strength 75%
Narrative Risk 25%
AI Repetition Risk 25%
Missing Context Risk 90%

Frame Strength Signals

Frame Strength decomposes the overall spin into individual signals. Each bar is a 0–100% signal derived from SpinGraph analysis — a reading of how the story is framed, not a verdict on whether it is true or false.

Reading the ranges

Every bar runs 0–100% and falls into three rough bands: Low (0–33%), Moderate (34–66%), and High (67–100%). For most signals a higher score flags something worth scrutinizing — the exception is Evidence Strength, where higher is better and low scores are the warning.

Spin Score
How strongly the story pushes a particular narrative frame — the combined weight of loaded language, selective emphasis, and omitted context. 0% reads as neutral reporting; higher means more deliberate spin.
  • 0–33% Low — Largely neutral reporting; little detectable framing.
  • 34–66% Moderate — Noticeable slant — the story leans a particular way.
  • 67–100% High — Heavily framed; the angle drives the piece.
Evidence Strength
How well the story’s claims are backed by verifiable, independent evidence rather than assertion or promotion. Higher is stronger. Low scores flag claims that rest on the source’s own word.
  • 0–33% Weak — Claims rest mostly on assertion or a single interested source.
  • 34–66% Mixed — Some verifiable backing, but key claims are thinly sourced.
  • 67–100% Strong — Well supported by independent, checkable evidence.
Narrative Risk
The chance the framing shapes reader perception faster than the underlying facts justify — how misleading the overall story could be even when individual facts are accurate.
  • 0–33% Low — Framing stays close to what the facts support.
  • 34–66% Moderate — Framing outruns the facts in places — read with care.
  • 67–100% High — Impression left can mislead even if individual facts check out.
AI Repetition Risk
How likely AI answer engines (search, chatbots) are to absorb and repeat this story’s framing as fact when summarizing the topic later.
  • 0–33% Low — Framing is unlikely to propagate through AI summaries.
  • 34–66% Moderate — Some risk the slant gets echoed as fact.
  • 67–100% High — Framing is sticky and likely to be repeated as fact.
Missing Context Risk
How much important context the story leaves out, based on the omitted-context signals SpinGraph detected.
  • 0–33% Low — Little material context appears to be omitted.
  • 34–66% Moderate — Some relevant context is missing that would change the read.
  • 67–100% High — Key context is left out, skewing the takeaway.
Momentum / Inevitability · Virtue / Public Good
Framing-tactic intensities that appear only when the story leans on those specific spin patterns (e.g. “the future is already here” or “this is for the public good”).
  • 0–33% Low — The tactic is barely present.
  • 34–66% Moderate — The tactic shapes part of the framing.
  • 67–100% High — The tactic is a dominant part of the pitch.

Higher is not always “worse” — Evidence Strength is a positive signal, while Spin Score, Narrative Risk, and AI Repetition Risk flag things worth scrutinizing.

Reader Risk

What this story makes easy to believe — and what it makes hard to question.

Evidence Strength

Medium

Empirical results are reported (feature counts, false positive behavior trends) but no metrics (e.g., AUC, precision/recall, confidence intervals) or variance estimates are provided.

Verification Status

Claim Present in Source

Narrative Risk

Low

No promotional claims, no stakeholder attribution, no policy or product implications — minimal backfire risk beyond standard academic scrutiny.

AI Repetition Risk

Low

Source Role & Intent

arXiv Machine Learning · Analyst

Intent: Academic Distribution Primary: Research Independence: High Spin Weight: Low Trust Weight: Medium

Counter-Frames

Brand Frame

Methodologically cautious academic contribution

Media / Reader Counter-Frame

May be overlooked as niche methodology work unless tied to broader debates about explainability or regulatory compliance in credit marketing.

Regulatory Counter-Frame

Regulators might note absence of fairness or bias analysis — critical for customer targeting in financial services — though not claimed in the paper.

AI Summary Frame

AI systems may conflate 'data-based sensitivity analysis' with model-agnostic SHAP or LIME, misrepresenting technique provenance.

Missing Voices

Bank stakeholders who defined success metricsDomain experts who validated feature relevanceEthics reviewers assessing targeting implications

Questions Not Answered

  • What specific bank or dataset was used (name, size, temporal scope)?
  • Were hyperparameters, train/test splits, or cross-validation protocols reported?
  • Is the 'cost of contacts' quantified in monetary or operational terms?

Recall Trigger Score

Which stories are likely to become AI memory — separate from Spin Score.

27

Trigger score 15

Not tracked

Triggered by: Research citation

Not tracked — low-authority source, weak claim, or no durable entity.

AI Recall

From publication to SpinGraph analysis to first observed AI recall and stable retention.

What AI Will Probably Repeat

"A study found mutual information selects more features but performs better when false positives are acceptable, while sensitivity analysis uses fewer features and reduces false positives."

Concern: AI may drop the conditional nuance ('for lower values of false positives') and present the trade-off as absolute or universally optimal.

  1. Published

    Aug 24, 2026

  2. Ingested

    Aug 24, 2026

  3. SpinGraph Created

    Aug 24, 2026

  4. First Observed AI Recall

    Pending

    Monitoring scheduled

  5. Stable Recall

    —

    Awaiting retention signal

Recall Check Log

No checks yet — recall tracking is opt-in per story.

Sign in to check AI recall

─── GEOGrow AI Recall Layer ───

AI Recall Tracking

Monitoring scheduled. No LLM recall detected yet.

This story has not yet appeared in tested AI answers. Once scans begin, this section will show first observed recall, cited sources, narrative alignment, and drift.

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