Above are papers related to the following paper: [Paper_ID] Paper #1123215. 	[Title] NP-Completeness of Minimum Rule Sets [Abstract] 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 to cover a broader spectrum of complexity measures and learning algorithms. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #288107. 	[Title] The Design and Evaluation of a Rule Induction Algorithm [Abstract] technical report BYU-CS-93-11 June 1993 Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #149139. 	[Title] The Minimum Feature Set Problem [Abstract] This paper appeared in Neural Networks 7 (1994), no. 3, pp. 491-494. Please summarise all the given information.

Paper #1123215 has the title `NP-Completeness of Minimum Rule Sets  
` and the abstract `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 to cover a broader spectrum of complexity measures and learning algorithms. 
`.
Question: Which category should Paper #1123215 be classified as ?
You can select one from ['Neural_Networks', 'Case_Based', 'Theory', 'Reinforcement_Learning', 'Probabilistic_Methods', 'Rule_Learning', 'Genetic_Algorithms'].


Paper #1123215 has the title `NP-Completeness of Minimum Rule Sets  
` and the abstract `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 to cover a broader spectrum of complexity measures and learning algorithms. 
`.
Question: Which category should Paper #1123215 be classified as ?
You can select one from ['Neural_Networks', 'Case_Based', 'Theory', 'Reinforcement_Learning', 'Probabilistic_Methods', 'Rule_Learning', 'Genetic_Algorithms'].

This paper should be classified into: Theory