Above are papers related to the following paper: [Paper_ID] Paper #135798. 	[Title] LINNEO A Classification Methodology for Ill-structured Domains [Abstract] In this work we present a classification methodology (LINNEO + ) to discover concepts from ill-structured domains and to organize hierarchies with them. In order to achieve this aim LINNEO + uses conceptual learning techniques and classification. The final target is to build knowledge bases after expert validation. Some techniques for the improvement of the results in the classification step are used, like biasing using partial expert knowledge (classification rules or causal and structural dependencies between attributes) or delayed cluster assignation of objects. Also some comparisons with a few well-known systems are shown. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #1108656. 	[Title] Rule Generation and Compaction in the wwtp [Abstract] In this paper we discuss our approach to learning classification rules from data. We sketch out two modules of our architecture, namely LINNEO + and GAR. LINNEO + , which is a knowledge acquisition tool for ill-structured domains automatically generating classes from examples that incrementally works with an unsupervised strategy. LINNEO + 's output, a representation of the conceptual structure of the domain in terms of classes, is the input to GAR that is used to generate a set of classification rules for the original training set. GAR can generate both conjunctive and disjunctive rules. Herein we present an application of these techniques to data obtained from a real wastewater treatment plant in order to help the construction of a rule base. This rule will be used for a knowledge-based system that aims to supervise the whole process. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #6318. 	[Title] Rule Induction and Instance-Based Learning: A Unified Approach [Abstract] This paper presents a new approach to inductive learning that combines aspects of instance-based learning and rule induction in a single simple algorithm. The RISE system searches for rules in a specific-to-general fashion, starting with one rule per training example, and avoids some of the difficulties of separate-and-conquer approaches by evaluating each proposed induction step globally, i.e., through an efficient procedure that is equivalent to checking the accuracy of the rule set as a whole on every training example. Classification is performed using a best-match strategy, and reduces to nearest-neighbor if all generalizations of instances were rejected. An extensive empirical study shows that RISE consistently achieves higher accuracies than state-of-the-art representatives of its "parent" paradigms (PEBLS and CN2), and also outperforms a decision-tree learner (C4.5) in 13 out of 15 test domains (in Please summarise all the given information.

Paper #135798 has the title `LINNEO A Classification Methodology for Ill-structured Domains  
` and the abstract `In this work we present a classification methodology (LINNEO + ) to discover concepts from ill-structured domains and to organize hierarchies with them. In order to achieve this aim LINNEO + uses conceptual learning techniques and classification. The final target is to build knowledge bases after expert validation. Some techniques for the improvement of the results in the classification step are used, like biasing using partial expert knowledge (classification rules or causal and structural dependencies between attributes) or delayed cluster assignation of objects. Also some comparisons with a few well-known systems are shown.
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Question: Which category should Paper #135798 be classified as ?
You can select one from ['Neural_Networks', 'Case_Based', 'Theory', 'Reinforcement_Learning', 'Probabilistic_Methods', 'Rule_Learning', 'Genetic_Algorithms'].


Paper #135798 has the title `LINNEO A Classification Methodology for Ill-structured Domains  
` and the abstract `In this work we present a classification methodology (LINNEO + ) to discover concepts from ill-structured domains and to organize hierarchies with them. In order to achieve this aim LINNEO + uses conceptual learning techniques and classification. The final target is to build knowledge bases after expert validation. Some techniques for the improvement of the results in the classification step are used, like biasing using partial expert knowledge (classification rules or causal and structural dependencies between attributes) or delayed cluster assignation of objects. Also some comparisons with a few well-known systems are shown.
`.
Question: Which category should Paper #135798 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: Case_Based