Above are papers related to the following paper: [Paper_ID] Paper #521251. 	[Title] A Yardstick for the Evaluation of Case-Based Classifiers [Abstract] This paper proposes that the generalisation capabilities of a case-based reasoning system can be evaluated by comparison with a `rote-learning' algorithm which uses a very simple generalisation strategy. Two such algorithms are defined, and expressions for their classification accuracy are derived as a function of the size of training sample. A series of experiments using artificial and `natural' data sets is described in which the learning curve for a case-based learner is compared with those for the apparently trivial rote-learning learning algorithms. The results show that in a number of `plausible' situations, the learning curves for a simple case-based learner and the `majority' rote-learner can barely be distinguished, although a domain is demonstrated where favourable performance from the case-based learner is observed. This suggests that the maxim of case-based reasoning that `similar problems have similar solutions' may be useful as the basis of a generalisation strategy only in selected domains. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #3232. 	[Title] The Power of Decision Tables [Abstract] We evaluate the power of decision tables as a hypothesis space for supervised learning algorithms. Decision tables are one of the simplest hypothesis spaces possible, and usually they are easy to understand. Experimental results show that on artificial and real-world domains containing only discrete features, IDTM, an algorithm inducing decision tables, can sometimes outperform state-of-the-art algorithms such as C4.5. Surprisingly, performance is quite good on some datasets with continuous features, indicating that many datasets used in machine learning either do not require these features, or that these features have few values. We also describe an incremental method for performing cross-validation that is applicable to incremental learning algorithms including IDTM. Using incremental cross-validation, it is possible to cross-validate a given dataset and IDTM in time that is linear in the number of instances, the number of features, and the number of label values. The time for incremental cross-validation is independent of the number of folds chosen, hence leave-one-out cross-validation and ten-fold cross-validation take the same time. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #66594. 	[Title] Automatic Parameter Selection by Minimizing Estimated Error [Abstract] We address the problem of finding the parameter settings that will result in optimal performance of a given learning algorithm using a particular dataset as training data. We describe a "wrapper" method, considering determination of the best parameters as a discrete function optimization problem. The method uses best-first search and cross-validation to wrap around the basic induction algorithm: the search explores the space of parameter values, running the basic algorithm many times on training and holdout sets produced by cross-validation to get an estimate of the expected error of each parameter setting. Thus, the final selected parameter settings are tuned for the specific induction algorithm and dataset being studied. We report experiments with this method on 33 datasets selected from the UCI and StatLog collections using C4.5 as the basic induction algorithm. At a 90% confidence level, our method improves the performance of C4.5 on nine domains, degrades performance on one, and is statistically indistinguishable from C4.5 on the rest. On the sample of datasets used for comparison, our method yields an average 13% relative decrease in error rate. We expect to see similar performance improvements when using our method with other machine learning al gorithms. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #521252. 	[Title] Similarity Metrics: A Formal Unification of Cardinal and Non-Cardinal Similarity Measures [Abstract] In [9] we introduced a formal framework for constructing ordinal similarity measures, and suggested how this might also be applied to cardinal measures. In this paper we will place this approach in a more general framework, called similarity metrics. In this framework, ordinal similarity metrics (where comparison returns a boolean value) can be combined with cardinal metrics (returning a numeric value) and, indeed, with metrics returning values of other types, to produce new metrics. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #346243. 	[Title] Inductive Bias in Case-Based Reasoning Systems [Abstract] In order to learn more about the behaviour of case-based reasoners as learning systems, we form-alise a simple case-based learner as a PAC learning algorithm, using the case-based representation hCB; i. We first consider a `naive' case-based learning algorithm CB1( H ) which learns by collecting all available cases into the case-base and which calculates similarity by counting the number of features on which two problem descriptions agree. We present results concerning the consistency of this learning algorithm and give some partial results regarding its sample complexity. We are able to characterise CB1( H ) as a `weak but general' learning algorithm. We then consider how the sample complexity of case-based learning can be reduced for specific classes of target concept by the application of inductive bias, or prior knowledge of the class of target concepts. Following recent work demonstrating how case-based learning can be improved by choosing a similarity measure appropriate to the concept being learnt, we define a second case-based learning `algorithm' CB2 which learns using the best possible similarity measure that might be inferred for the chosen target concept. While CB2 is not an executable learning strategy (since the chosen similarity measure is defined in terms of a priori knowledge of the actual target concept) it allows us to assess in the limit the maximum possible contribution of this approach to case-based learning. Also, in addition to illustrating the role of inductive bias, the definition of CB2 simplifies the general problem of establishing which functions might be represented in the form hCB; i. Reasoning about the case-based representation in this special case has therefore been a little more straight-forward than in the general case of CB1( H ), allowing more substantial results regarding representable functions and sample complexity to be presented for CB2. In assessing these results, we are forced to conclude that case-based learning is not the best approach to learning the chosen concept space (the space of monomial functions). We discuss, however, how our study has demonstrated, in the context of case-based learning, the operation of concepts well known in machine learning such as inductive bias and the trade-off between computational complexity and sample complexity. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #6343. 	[Title] A Review and Empirical Evaluation of Feature Weighting Methods for a Class of Lazy Learning Algorithms [Abstract] Many lazy learning algorithms are derivatives of the k-nearest neighbor (k-NN) classifier, which uses a distance function to generate predictions from stored instances. Several studies have shown that k-NN's performance is highly sensitive to the definition of its distance function. Many k-NN variants have been proposed to reduce this sensitivity by parameterizing the distance function with feature weights. However, these variants have not been categorized nor empirically compared. This paper reviews a class of weight-setting methods for lazy learning algorithms. We introduce a framework for distinguishing these methods and empirically compare them. We observed four trends from our experiments and conducted further studies to highlight them. Our results suggest that methods which use performance feedback to assign weight settings demonstrated three advantages over other methods: they require less pre-processing, perform better in the presence of interacting features, and generally require less training data to learn good settings. We also found that continuous weighting methods tend to outperform feature selection algorithms for tasks where some features are useful but less important than others. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #1125992. 	[Title] Towards a Theory of Optimal Similarity Measures way of learning a similarity measure from the [Abstract] The effectiveness of a case-based reasoning system is known to depend critically on its similarity measure. However, it is not clear whether there are elusive and esoteric similarity measures which might improve the performance of a case-based reasoner if substituted for the more commonly used measures. This paper therefore deals with the problem of choosing the best similarity measure, in the limited context of instance-based learning of classifications of a discrete example space. We consider both `fixed' similarity measures and `learnt' ones. In the former case, we give a definition of a similarity measure which we believe to be `optimal' w.r.t. the current prior distribution of target concepts and prove its optimality within a restricted class of similarity measures. We then show how this `optimal' similarity measure is instantiated by some specific prior distributions, and conclude that a very simple similarity measure is as good as any other in these cases. In a further section, we then show how our definition leads naturally to a conjecture about the Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #20942. 	[Title] Decision Tree Induction Based on Efficient Tree Restructuring [Abstract] The ability to restructure a decision tree efficiently enables a variety of approaches to decision tree induction that would otherwise be prohibitively expensive. Two such approaches are described here, one being incremental tree induction (ITI), and the other being non-incremental tree induction using a measure of tree quality instead of test quality (DMTI). These approaches and several variants offer new computational and classifier characteristics that lend themselves to particular applications. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #1106330. 	[Title] Induction of Condensed Determinations [Abstract] In this paper we suggest determinations as a representation of knowledge that should be easy to understand. We briefly review determinations, which can be displayed in a tabular format, and their use in prediction, which involves a simple matching process. We describe ConDet, an algorithm that uses feature selection to construct determinations from training data, augmented by a condensation process that collapses rows to produce simpler structures. We report experiments that show condensation reduces complexity with no loss of accuracy, then discuss ConDet's relation to other work and outline directions for future studies. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #20850. 	[Title] Compression-Based Feature Subset Selection  Keywords: Minimum Description Length Principle, Cross Validation, Noise [Abstract] Irrelevant and redundant features may reduce both predictive accuracy and comprehensibility of induced concepts. Most common Machine Learning approaches for selecting a good subset of relevant features rely on cross-validation. As an alternative, we present the application of a particular Minimum Description Length (MDL) measure to the task of feature subset selection. Using the MDL principle allows taking into account all of the available data at once. The new measure is information-theoretically plausible and yet still simple and therefore efficiently computable. We show empirically that this new method for judging the value of feature subsets is more efficient than and performs at least as well as methods based on cross-validation. Domains with both a large number of training examples and a large number of possible features yield the biggest gains in efficiency. Thus our new approach seems to scale up better to large learning problems than previous methods. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #3220. 	[Title] A Weighted Nearest Neighbor Algorithm for Learning with Symbolic Features [Abstract] In the past, nearest neighbor algorithms for learning from examples have worked best in domains in which all features had numeric values. In such domains, the examples can be treated as points and distance metrics can use standard definitions. In symbolic domains, a more sophisticated treatment of the feature space is required. We introduce a nearest neighbor algorithm for learning in domains with symbolic features. Our algorithm calculates distance tables that allow it to produce real-valued distances between instances, and attaches weights to the instances to further modify the structure of feature space. We show that this technique produces excellent classification accuracy on three problems that have been studied by machine learning researchers: predicting protein secondary structure, identifying DNA promoter sequences, and pronouncing English text. Direct experimental comparisons with the other learning algorithms show that our nearest neighbor algorithm is comparable or superior in all three domains. In addition, our algorithm has advantages in training speed, simplicity, and perspicuity. We conclude that experimental evidence favors the use and continued development of nearest neighbor algorithms for domains such as the ones studied here. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #521207. 	[Title] A Formal Analysis of Case Base Retrieval [Abstract] Case based systems typically retrieve cases from the case base by applying similarity measures. The measures are usually constructed in an ad hoc manner. This report presents a toolbox for the systematic construction of similarity measures. In addition to paving the way to a design methodology for similarity measures, this systematic approach facilitates the identification of opportunities for parallelisation in case base retrieval. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #1128536. 	[Title] MLC Tutorial A Machine Learning library of C classes. [Abstract]  Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #39130. 	[Title] Average-Case Analysis of a Nearest Neighbor Algorithm [Abstract] In this paper we present an average-case analysis of the nearest neighbor algorithm, a simple induction method that has been studied by many researchers. Our analysis assumes a conjunctive target concept, noise-free Boolean attributes, and a uniform distribution over the instance space. We calculate the probability that the algorithm will encounter a test instance that is distance d from the prototype of the concept, along with the probability that the nearest stored training case is distance e from this test instance. From this we compute the probability of correct classification as a function of the number of observed training cases, the number of relevant attributes, and the number of irrelevant attributes. We also explore the behavioral implications of the analysis by presenting predicted learning curves for artificial domains, and give experimental results on these domains as a check on our reasoning. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #1123068. 	[Title] Targeting Business Users with Decision Table Classifiers [Abstract] Business users and analysts commonly use spreadsheets and 2D plots to analyze and understand their data. On-line Analytical Processing (OLAP) provides these users with added flexibility in pivoting data around different attributes and drilling up and down the multi-dimensional cube of aggregations. Machine learning researchers, however, have concentrated on hypothesis spaces that are foreign to most users: hyper-planes (Perceptrons), neural networks, Bayesian networks, decision trees, nearest neighbors, etc. In this paper we advocate the use of decision table classifiers that are easy for line-of-business users to understand. We describe several variants of algorithms for learning decision tables, compare their performance, and describe a visualization mechanism that we have implemented in MineSet. The performance of decision tables is comparable to other known algorithms, such as C4.5/C5.0, yet the resulting classifiers use fewer attributes and are more comprehensible. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #521183. 	[Title] Formalising the knowledge content of case memory systems [Abstract] Discussions of case-based reasoning often reflect an implicit assumption that a case memory system will become better informed, i.e. will increase in knowledge, as more cases are added to the case-base. This paper considers formalisations of this `knowledge content' which are a necessary preliminary to more rigourous analysis of the performance of case-based reasoning systems. In particular we are interested in modelling the learning aspects of case-based reasoning in order to study how the performance of a case-based reasoning system changes as it accumlates problem-solving experience. The current paper presents a `case-base semantics' which generalises recent formalisations of case-based classification. Within this framework, the paper explores various issues in assuring that these sematics are well-defined, and illustrates how the knowledge content of the case memory system can be seen to reside in both the chosen similarity measure and in the cases of the case-base. Please summarise all the given information.

Paper #521251 has the title `A Yardstick for the Evaluation of Case-Based Classifiers  
` and the abstract `This paper proposes that the generalisation capabilities of a case-based reasoning system can be evaluated by comparison with a `rote-learning' algorithm which uses a very simple generalisation strategy. Two such algorithms are defined, and expressions for their classification accuracy are derived as a function of the size of training sample. A series of experiments using artificial and `natural' data sets is described in which the learning curve for a case-based learner is compared with those for the apparently trivial rote-learning learning algorithms. The results show that in a number of `plausible' situations, the learning curves for a simple case-based learner and the `majority' rote-learner can barely be distinguished, although a domain is demonstrated where favourable performance from the case-based learner is observed. This suggests that the maxim of case-based reasoning that `similar problems have similar solutions' may be useful as the basis of a generalisation strategy only in selected domains.
`.
Question: Which category should Paper #521251 be classified as ?
You can select one from ['Neural_Networks', 'Case_Based', 'Theory', 'Reinforcement_Learning', 'Probabilistic_Methods', 'Rule_Learning', 'Genetic_Algorithms'].


Paper #521251 has the title `A Yardstick for the Evaluation of Case-Based Classifiers  
` and the abstract `This paper proposes that the generalisation capabilities of a case-based reasoning system can be evaluated by comparison with a `rote-learning' algorithm which uses a very simple generalisation strategy. Two such algorithms are defined, and expressions for their classification accuracy are derived as a function of the size of training sample. A series of experiments using artificial and `natural' data sets is described in which the learning curve for a case-based learner is compared with those for the apparently trivial rote-learning learning algorithms. The results show that in a number of `plausible' situations, the learning curves for a simple case-based learner and the `majority' rote-learner can barely be distinguished, although a domain is demonstrated where favourable performance from the case-based learner is observed. This suggests that the maxim of case-based reasoning that `similar problems have similar solutions' may be useful as the basis of a generalisation strategy only in selected domains.
`.
Question: Which category should Paper #521251 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