Rule Extraction in Machine Learning: Chat Incremental Pattern Constructor
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arXiv
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| Format: | Preprint |
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2022
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| _version_ | 1866916069132730368 |
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| author | Nwokocha, Caleb Princewill |
| author_facet | Nwokocha, Caleb Princewill |
| contents | Rule extraction is a central problem in interpretable machine learning because it seeks to convert opaque predictive behavior into human-readable symbolic structure. This paper presents Chat Incremental Pattern Constructor (ChatIPC), a lightweight incremental symbolic learning system that extracts ordered token-transition rules from text, enriches them with definition-based expansion, and constructs responses by similarity-guided candidate selection. The system may be viewed as a rule extractor operating over a token graph rather than a conventional classifier. I formalize the knowledge base, definition expansion, candidate scoring, repetition control, English-rule heuristics, and response construction mechanisms used by ChatIPC. I further situate the method within the literature on rule extraction, decision tree induction, association rules, interpretable machine learning, and sequence construction. The updated implementation is also reviewed in detail: it parses an embedded dictionary, normalizes lexical keys, caches definition tokens and part-of-speech tags, computes Jaccard scores on bitsets, applies heuristic linguistic bonuses, and persists the knowledge base with a versioned binary format. The paper emphasizes mathematical formulation and algorithmic clarity, and it provides pseudocode for the learning, scoring, and construction algorithms. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2208_00335 |
| institution | arXiv |
| publishDate | 2022 |
| record_format | arxiv |
| spellingShingle | Rule Extraction in Machine Learning: Chat Incremental Pattern Constructor Nwokocha, Caleb Princewill Machine Learning 68T05 (Primary), 68T30, 68T50 (Secondary) I.2.6; I.2.7; I.2.4 Rule extraction is a central problem in interpretable machine learning because it seeks to convert opaque predictive behavior into human-readable symbolic structure. This paper presents Chat Incremental Pattern Constructor (ChatIPC), a lightweight incremental symbolic learning system that extracts ordered token-transition rules from text, enriches them with definition-based expansion, and constructs responses by similarity-guided candidate selection. The system may be viewed as a rule extractor operating over a token graph rather than a conventional classifier. I formalize the knowledge base, definition expansion, candidate scoring, repetition control, English-rule heuristics, and response construction mechanisms used by ChatIPC. I further situate the method within the literature on rule extraction, decision tree induction, association rules, interpretable machine learning, and sequence construction. The updated implementation is also reviewed in detail: it parses an embedded dictionary, normalizes lexical keys, caches definition tokens and part-of-speech tags, computes Jaccard scores on bitsets, applies heuristic linguistic bonuses, and persists the knowledge base with a versioned binary format. The paper emphasizes mathematical formulation and algorithmic clarity, and it provides pseudocode for the learning, scoring, and construction algorithms. |
| title | Rule Extraction in Machine Learning: Chat Incremental Pattern Constructor |
| topic | Machine Learning 68T05 (Primary), 68T30, 68T50 (Secondary) I.2.6; I.2.7; I.2.4 |
| url | https://arxiv.org/abs/2208.00335 |