Title: Extracting Rules in Machine Learning via the Chat Incremental Pattern Constructor

Abstract:

Rule extraction addresses a pivotal challenge in interpretable machine learning by transforming opaque predictive models into human-readable symbolic frameworks. This study introduces the Chat Incremental Pattern Constructor (ChatIPC), a lightweight system designed for incremental symbolic learning. ChatIPC functions by extracting ordered token-transition rules from textual data, enhancing these rules through definition-based expansion, and generating responses via similarity-guided candidate selection. Rather than operating as a traditional classifier, the system can be conceptualized as a rule extractor that works over a token graph.

The paper provides a formalization of the core mechanisms employed by ChatIPC, including its knowledge base structure, definition expansion processes, candidate scoring methods, repetition control measures, English-rule heuristics, and response construction protocols. Additionally, the method is contextualized within existing research on rule extraction, decision tree induction, association rules, interpretable machine learning, and sequence construction.

A detailed review of the updated implementation is also included. The system parses an embedded dictionary, normalizes lexical keys, and caches part-of-speech tags alongside definition tokens. It calculates Jaccard scores using bitsets, applies heuristic linguistic bonuses, and stores the knowledge base in a versioned binary format. Emphasizing mathematical rigor and algorithmic transparency, the paper presents pseudocode for the learning, scoring, and construction algorithms.

Source: arXiv Generated at: 2026-06-02 00:00:00 UTC