Above are papers related to the following paper: [Paper_ID] Paper #4553. 	[Title] Learning Classification Rules Using Lattices [Abstract] This paper presents a novel induction algorithm, Rulearner, which induces classification rules using a Galois lattice as an explicit map through the search space of rules. The construction of lattices from data is initially discussed and the use of these structures in inducing classification rules is examined. The Rulearner system is shown to compare favorably with commonly used symbolic learning methods which use heursitics rather than an explicit map to guide their search through the rule space. Furthermore, our learning system is shown to be robust in the presence of noisy data. The Rulearner system is also capable of learning both decision lists as well as unordered rule sets and thus allows for comparisons of these different learning paradigms within the same algorithmic framework. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #1102548. 	[Title] IGLUE An Instance-based Learning System over Lattice Theory [Abstract] Concept learning is one of the most studied areas in machine learning. A lot of work in this domain deals with decision trees. In this paper, we are concerned with a different kind of technique based on Galois lattices or concept lattices. We present a new semi-lattice based system, IGLUE, that uses the entropy function with a top-down approach to select concepts during the lattice construction. Then IGLUE generates new relevant numerical features by transforming initial boolean features over these concepts. IGLUE uses the new features to redescribe examples. Finally, IGLUE applies the Mahanalobis distance as a similarity measure between examples. Keywords : Multistrategy Learning, Instance-Based Learning, Galois lattice, Feature transformation Please summarise all the given information.

Paper #4553 has the title `Learning Classification Rules Using Lattices  
` and the abstract `This paper presents a novel induction algorithm, Rulearner, which induces classification rules using a Galois lattice as an explicit map through the search space of rules. The construction of lattices from data is initially discussed and the use of these structures in inducing classification rules is examined. The Rulearner system is shown to compare favorably with commonly used symbolic learning methods which use heursitics rather than an explicit map to guide their search through the rule space. Furthermore, our learning system is shown to be robust in the presence of noisy data. The Rulearner system is also capable of learning both decision lists as well as unordered rule sets and thus allows for comparisons of these different learning paradigms within the same algorithmic framework.
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Question: Which category should Paper #4553 be classified as ?
You can select one from ['Neural_Networks', 'Case_Based', 'Theory', 'Reinforcement_Learning', 'Probabilistic_Methods', 'Rule_Learning', 'Genetic_Algorithms'].


Paper #4553 has the title `Learning Classification Rules Using Lattices  
` and the abstract `This paper presents a novel induction algorithm, Rulearner, which induces classification rules using a Galois lattice as an explicit map through the search space of rules. The construction of lattices from data is initially discussed and the use of these structures in inducing classification rules is examined. The Rulearner system is shown to compare favorably with commonly used symbolic learning methods which use heursitics rather than an explicit map to guide their search through the rule space. Furthermore, our learning system is shown to be robust in the presence of noisy data. The Rulearner system is also capable of learning both decision lists as well as unordered rule sets and thus allows for comparisons of these different learning paradigms within the same algorithmic framework.
`.
Question: Which category should Paper #4553 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: Rule_Learning