This paper proposes a new paradigm for learning set of independent logical rules in disjunctive normal form as an interpretable model classification. We consider the problem decision rule training neural network specific, yet very simple two-layer architecture. Each neuron first layer directly maps to if-then after training, and output second disjunction set. Our representation neurons this ena...

The two dominant schemes for rule learning C and RIPPER both operate in two stages First they induce an initial rule set and then they re ne it using a rather com plex optimization stage that discards C or adjusts RIPPER individual rules to make them work better together In con trast this paper shows how good rule sets can be learned one rule at a time with out any need for global optimization ...

This paper concerns a relationship between Bayes’ inference rule and decision rules from the rough set perspective. In statistical inference based on the Bayes’ rule it is assumed that some prior knowledge (prior probability) about some parameters without knowledge about the data is given first. Next the posterior probability is computed by employing the available data. The posterior probabilit...

This paper is about the semantics of production rule sets, a language used to model asynchronous digital circuits. Two formal semantics are developed and proved equivalent: a set-theoretic semantics that improves upon an earlier effort of ours, and an executable semantics in rewriting logic. The set-theoretic semantics is especially suited to meta-level proofs about production rule sets, wherea...

Model trees—decision trees with linear models at the leaf nodes—have recently emerged as an accurate method for numeric prediction that produces understandable models. However, it is known that decision lists—ordered sets of If-Then rules—have the potential to be more compact and therefore more understandable than their tree counterparts. We present an algorithm for inducing simple, accurate de...

Rule induction systems seek to generate rule sets which are optimal in the complexity of the rule set. This paper develops a formal proof of the NP-Completeness of the problem of generating the simplest rule set (MIN RS) which accurately predicts examples in the training set for a particular type of generalization algorithm algorithm and complexity measure. The proof is then informally extended...

ASSOCIATION RULE see extraction methods have been developed as the main methods for mining of real life data in particular in Basket Data Analysis In this paper we present a novel ap proach to generation of association rule based on Rough Set and Boolean reasoning methods We show the relationship between the problems of association rule extraction for transaction data and relative reducts or re...

We present a technique for evaluating classifications by geometric comparison of rule sets. Rules are represented as objects in an n-dimensional hyperspace. The similarity of classes is computed from the overlap of the geometric class descriptions. The system produces a correlation matrix that indicates the degree of similarity between each pair of classes. The technique can be applied to class...