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Record W2135420268 · doi:10.1109/grc.2007.40

Naïve Bayes Text Classifier

2007· article· en· W2135420268 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

Venue2007 IEEE International Conference on Granular Computing (GRC 2007) · 2007
Typearticle
Languageen
FieldComputer Science
TopicText and Document Classification Technologies
Canadian institutionsAcadia University
Fundersnot available
KeywordsNaive Bayes classifierComputer scienceBayes' theoremSupport vector machineArtificial intelligenceMachine learningDetectorClassifier (UML)Bayesian probabilityInformation retrievalData mining

Abstract

fetched live from OpenAlex

Text classification algorithms, such SVM, and Naïve Bayes, have been developed to build up search engines and construct spam email filters. As a simple yet powerful sample of Bayesian Theorem, Naïve Bayes shows advantages in text classification yielding satisfactory results. In this paper, a spam email detector is developed using Naïve Bayes algorithm. We use pre-classified emails (priory knowledge) to train the spam email detector. With the model generated from the training step, the detector is able to decide whether an email is a spam email or an ordinary email.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.929
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0030.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.002

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.071
GPT teacher head0.329
Teacher spread0.259 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it