Above are papers related to the following paper: [Paper_ID] Paper #14531. 	[Title] Context-Sensitive Feature Selection for Lazy Learners [Abstract]  Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #1117184. 	[Title] NACODAE: Navy Conversational Decision Aids Environment [Abstract] This report documents NACODAE, the Navy Conversational Decision Aids Environment being developed at the Navy Center for Applied Research in Artificial Intelligence (NCARAI), which is a branch of the Naval Research Laboratory. NA-CODAE is a software prototype that is being developed under the Practical Advances in Case-Based Reasoning project, which is funded by the Office for Naval Research, for the purpose of assisting Navy and other DoD personnel in decision aids tasks such as system maintenance, operational training, crisis response planning, logistics, fault diagnosis, target classification, and meteorological nowcasting. Implemented in Java, NACODAE can be used on any machine containing a Java virtual machine (e.g., PCs, Unix). This document describes and exemplifies NACODAE's capabilities. Our goal is to transition this tool to operational personnel, and to continue its enhancement through user feedback and by testing recent research advances in case-based reasoning and related areas. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #17798. 	[Title] Using Decision Trees to Improve Case-Based Learning [Abstract] This paper shows that decision trees can be used to improve the performance of case-based learning (CBL) systems. We introduce a performance task for machine learning systems called semi-flexible prediction that lies between the classification task performed by decision tree algorithms and the flexible prediction task performed by conceptual clustering systems. In semi-flexible prediction, learning should improve prediction of a specific set of features known a priori rather than a single known feature (as in classification) or an arbitrary set of features (as in conceptual clustering). We describe one such task from natural language processing and present experiments that compare solutions to the problem using decision trees, CBL, and a hybrid approach that combines the two. In the hybrid approach, decision trees are used to specify the features to be included in k-nearest neighbor case retrieval. Results from the experiments show that the hybrid approach outperforms both the decision tree and case-based approaches as well as two case-based systems that incorporate expert knowledge into their case retrieval algorithms. Results clearly indicate that decision trees can be used to improve the performance of CBL systems and do so without reliance on potentially expensive expert knowledge. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #60682. 	[Title] ICML-96 Workshop "Learning in context-sensitive domains" Bari, Italy. Dynamically Adjusting Concepts to Accommodate Changing Contexts [Abstract] In concept learning, objects in a domain are grouped together based on similarity as determined by the attributes used to describe them. Existing concept learners require that this set of attributes be known in advance and presented in entirety before learning begins. Additionally, most systems do not possess mechanisms for altering the attribute set after concepts have been learned. Consequently, a veridical attribute set relevant to the task for which the concepts are to be used must be supplied at the onset of learning, and in turn, the usefulness of the concepts is limited to the task for which the attributes were originally selected. In order to efficiently accommodate changing contexts, a concept learner must be able to alter the set of descriptors without discarding its prior knowledge of the domain. We introduce the notion of attribute-incrementation, the dynamic modification of the attribute set used to describe instances in a problem domain. We have implemented the capability in a concept learning system that has been evaluated along several dimensions using an existing concept formation system for com parison. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #1105932. 	[Title] An adaptation of Relief for attribute estimation in regression [Abstract] Heuristic measures for estimating the quality of attributes mostly assume the independence of attributes so in domains with strong dependencies between attributes their performance is poor. Relief and its extension ReliefF are capable of correctly estimating the quality of attributes in classification problems with strong dependencies between attributes. By exploiting local information provided by different contexts they provide a global view. We present the analysis of Reli-efF which lead us to its adaptation to regression (continuous class) problems. The experiments on artificial and real-world data sets show that Re-gressional ReliefF correctly estimates the quality of attributes in various conditions, and can be used for non-myopic learning of the regression trees. Regressional ReliefF and ReliefF provide a unified view on estimating the attribute quality in regression and classification. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #359067. 	[Title] Supporting Conversational Case-Based Reasoning in an Integrated Reasoning Framework  Conversational Case-Based Reasoning [Abstract] Conversational case-based reasoning (CCBR) has been successfully used to assist in case retrieval tasks. However, behavioral limitations of CCBR motivate the search for integrations with other reasoning approaches. This paper briefly describes our group's ongoing efforts towards enhancing the inferencing behaviors of a conversational case-based reasoning development tool named NaCoDAE. In particular, we focus on integrating NaCoDAE with machine learning, model-based reasoning, and generative planning modules. This paper defines CCBR, briefly summarizes the integrations, and explains how they enhance the overall system. Our research focuses on enhancing the performance of conversational case-based reasoning (CCBR) systems (Aha & Breslow, 1997). CCBR is a form of case-based reasoning where users initiate problem solving conversations by entering an initial problem description in natural language text. This text is assumed to be a partial rather than a complete problem description. The CCBR system then assists in eliciting refinements of this description and in suggesting solutions. Its primary purpose is to provide a focus of attention for the user so as to quickly provide a solution(s) for their problem. Figure 1 summarizes the CCBR problem solving cycle. Cases in a CCBR library have three components: Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #25772. 	[Title] Flexible Metric Nearest Neighbor Classiflcation [Abstract] The K-nearest-neighbor decision rule assigns an object of unknown class to the plurality class among the K labeled "training" objects that are closest to it. Closeness is usually deflned in terms of a metric distance on the Euclidean space with the input measurement variables as axes. The metric chosen to deflne this distance can strongly efiect performance. An optimal choice depends on the problem at hand as characterized by the respective class distributions on the input measurement space, and within a given problem, on the location of the unknown object in that space. In this paper new types of K-nearest-neighbor procedures are described that estimate the local relevance of each input variable, or their linear combinations, for each individual point to be classifled. This information is then used to separately customize the metric used to deflne distance from that object in flnding its nearest neighbors. These procedures are a hybrid between regular K-nearest-neighbor methods and treestructured recursive partitioning techniques popular in statistics and machine learning. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #1152308. 	[Title] Learning to Refine Case Libraries: [Abstract] Initial Results Abstract. Conversational case-based reasoning (CBR) systems, which incrementally extract a query description through a user-directed conversation, are advertised for their ease of use. However, designing large case libraries that have good performance (i.e., precision and querying efficiency) is difficult. CBR vendors provide guidelines for designing these libraries manually, but the guidelines are difficult to apply. We describe an automated inductive approach that revises conversational case libraries to increase their conformance with design guidelines. Revision increased performance on three conversational case libraries. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #53942. 	[Title] Simplifying Decision Trees: A Survey [Abstract] Induced decision trees are an extensively-researched solution to classification tasks. For many practical tasks, the trees produced by tree-generation algorithms are not comprehensible to users due to their size and complexity. Although many tree induction algorithms have been shown to produce simpler, more comprehensible trees (or data structures derived from trees) with good classification accuracy, tree simplification has usually been of secondary concern relative to accuracy and no attempt has been made to survey the literature from the perspective of simplification. We present a framework that organizes the approaches to tree simplification and summarize and critique the approaches within this framework. The purpose of this survey is to provide researchers and practitioners with a concise overview of tree-simplification approaches and insight into their relative capabilities. In our final discussion, we briefly describe some empirical findings and discuss the application of tree induction algorithms to case retrieval in case-based reasoning systems. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #592830. 	[Title] Context-sensitive attribute estimation in regression [Abstract] One of key issues in both discrete and continuous class prediction and in machine learning in general seems to be the problem of estimating the quality of attributes. Heuristic measures mostly assume independence of attributes so their use is non-optimal in domains with strong dependencies between attributes. For the same reason they are also mostly unable to recognize context dependent features. Relief and its extension Re-liefF are statistical methods capable of correctly estimating the quality of attributes in classification problems with strong dependencies between attributes. By exploiting local information provided by different contexts they provide a global view and recognize contextual attributes. After the analysis of ReliefF we have extended it to continuous class problems. Regressional ReliefF (RReliefF) and ReliefF provide a unified view on estimating attribute quality. The experiments show that RReliefF correctly estimates the quality of attributes, recognizes the contextual attributes and can be used for non myopic learning of the regression trees. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #1103038. 	[Title] The Management of Context-Sensitive Features: A Review of Strategies [Abstract] In this paper, we review five heuristic strategies for handling context-sensitive features in supervised machine learning from examples. We discuss two methods for recovering lost (implicit) contextual information. We mention some evidence that hybrid strategies can have a synergetic effect. We then show how the work of several machine learning researchers fits into this framework. While we do not claim that these strategies exhaust the possibilities, it appears that the framework includes all of the techniques that can be found in the published literature on context sensitive learning. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #75969. 	[Title] Employing Linear Regression in Regression Tree Leaves [Abstract] The advantage of using linear regression in the leaves of a regression tree is analysed in the paper. It is carried out how this modification affects the construction, pruning and interpretation of a regression tree. The modification is tested on artificial and real-life domains. The results show that the modification is beneficial as it leads to smaller classification errors of induced regression trees. Keywords: machine learning, TDIDT, regression, linear regression, Bayesian approach. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #14529. 	[Title] Concept Learning and Flexible Weighting [Abstract] We previously introduced an exemplar model, named GCM-ISW, that exploits a highly flexible weighting scheme. Our simulations showed that it records faster learning rates and higher asymptotic accuracies on several artificial categorization tasks than models with more limited abilities to warp input spaces. This paper extends our previous work; it describes experimental results that suggest human subjects also invoke such highly flexible schemes. In particular, our model provides significantly better fits than models with less flexibility, and we hypothesize that humans selectively weight attributes depending on an item's location in the input space. We need more flexible models Many theories of human concept learning posit that concepts are represented by prototypes (Reed, 1972) or exemplars (Medin & Schaffer, 1978). Prototype models represent concepts by the "best example" or "central tendency" of the concept. 1 A new item belongs in a category C if it is relatively similar to C's prototype. Prototype models are relatively inflexible; they discard a great deal of information that people use during concept learning (e.g., the number of exemplars in a concept (Homa & Cultice, 1984), the variability of features (Fried & Holyoak, 1984), correlations between features (Medin et al., 1982), and the particular exemplars used (Whittlesea, 1987)). of concept learning Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #239810. 	[Title] A Model-Based Approach for Supporting Dialogue Inferencing in a Conversational Case-Based Reasoner [Abstract] Conversational case-based reasoning (CCBR) is a form of interactive case-based reasoning where users input a partial problem description (in text). The CCBR system responds with a ranked solution display, which lists the solutions of stored cases whose problem descriptions best match the user's, and a ranked question display, which lists the unanswered questions in these cases. Users interact with these displays, either refining their problem description by answering selected questions, or selecting a solution to apply. CCBR systems should support dialogue inferencing; they should infer answers to questions that are implied by the problem description. Otherwise, questions will be listed that the user believes they have already answered. The standard approach to dialogue inferencing allows case library designers to insert rules that define implications between the problem description and unanswered questions. However, this approach imposes substantial knowledge engineering requirements. We introduce an alternative approach whereby an intelligent assistant guides the designer in defining a model of their case library, from which implication rules are derived. We detail this approach, its benefits, and explain how it can be supported through an integration with Parka-DB, a fast relational database system. We will evaluate our approach in the context of our CCBR system, named NaCoDAE. This paper appeared at the 1998 AAAI Spring Symposium on Multimodal Reasoning, and is NCARAI TR AIC-97-023. We introduce an integrated reasoning approach in which a model-based reasoning component performs an important inferencing role in a conversational case-based reasoning (CCBR) system named NaCoDAE (Breslow & Aha, 1997) (Figure 1). CCBR is a form of case-based reasoning where users enter text queries describing a problem and the system assists in eliciting refinements of it (Aha & Breslow, 1997). Cases have three components: Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #75972. 	[Title] Overcoming the myopia of inductive learning algorithms with RELIEFF [Abstract] Current inductive machine learning algorithms typically use greedy search with limited looka-head. This prevents them to detect significant conditional dependencies between the attributes that describe training objects. Instead of myopic impurity functions and lookahead, we propose to use RELI-EFF, an extension of RELIEF developed by Kira and Rendell [10], [11], for heuristic guidance of inductive learning algorithms. We have reimplemented Assistant, a system for top down induction of decision trees, using RELIEFF as an estimator of attributes at each selection step. The algorithm is tested on several artificial and several real world problems and the results are compared with some other well known machine learning algorithms. Excellent results on artificial data sets and two real world problems show the advantage of the presented approach to inductive learning. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #1119178. 	[Title] Correcting for Length Biasing in Conversational Case Scoring [Abstract] Inference's conversational case-based reasoning (CCBR) approach, embedded in the CBR Content Navigator line of products, is susceptible to a bias in its case scoring algorithm. In particular, shorter cases tend to be given higher scores, assuming all other factors are held constant. This report summarizes our investigation for mediating this bias. We introduce an approach for eliminating this bias and evaluate how it affects retrieval performance for six case libraries. We also suggest explanations for these results, and note the limitations of our study. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #14545. 	[Title] A Patient-Adaptive Neural Network ECG Patient Monitoring Algorithm [Abstract] The patient-adaptive classifier was compared with a well-established baseline algorithm on six major databases, consisting of over 3 million heartbeats. When trained on an initial 77 records and tested on an additional 382 records, the patient-adaptive algorithm was found to reduce the number of Vn errors on one channel by a factor of 5, and the number of Nv errors by a factor of 10. We conclude that patient adaptation provides a significant advance in classifying normal vs. ventricular beats for ECG Patient Monitoring. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #14549. 	[Title] Recognition and Exploitation of Contextual Clues via Incremental Meta-Learning (Extended Version) [Abstract] Daily experience shows that in the real world, the meaning of many concepts heavily depends on some implicit context, and changes in that context can cause more or less radical changes in the concepts. Incremental concept learning in such domains requires the ability to recognize and adapt to such changes. This paper presents a solution for incremental learning tasks where the domain provides explicit clues as to the current context (e.g., attributes with characteristic values). We present a general two-level learning model, and its realization in a system named MetaL(B), that can learn to detect certain types of contextual clues, and can react accordingly when a context change is suspected. The model consists of a base level learner that performs the regular on-line learning and classification task, and a meta-learner that identifies potential contextual clues. Context learning and detection occur during regular on-line learning, without separate training phases for context recognition. Experiments with synthetic domains as well as a `real-world' problem show that MetaL(B) is robust in a variety of dimensions and produces substantial improvement over simple object-level learning in situations with changing contexts. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #39127. 	[Title] Estimating Attributes: Analysis and Extensions of RELIEF [Abstract] In the context of machine learning from examples this paper deals with the problem of estimating the quality of attributes with and without dependencies among them. Kira and Rendell (1992a,b) developed an algorithm called RELIEF, which was shown to be very efficient in estimating attributes. Original RELIEF can deal with discrete and continuous attributes and is limited to only two-class problems. In this paper RELIEF is analysed and extended to deal with noisy, incomplete, and multi-class data sets. The extensions are verified on various artificial and one well known real-world problem. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #56167. 	[Title] Refining Conversational Case Libraries [Abstract] Conversational case-based reasoning (CBR) shells (e.g., Inference's CBR Express) are commercially successful tools for supporting the development of help desk and related applications. In contrast to rule-based expert systems, they capture knowledge as cases rather than more problematic rules, and they can be incrementally extended. However, rather than eliminate the knowledge engineering bottleneck, they refocus it on case engineering, the task of carefully authoring cases according to library design guidelines to ensure good performance. Designing complex libraries according to these guidelines is difficult; software is needed to assist users with case authoring. We describe an approach for revising case libraries according to design guidelines, its implementation in Clire, and empirical results showing that, under some conditions, this approach can improve conversational CBR performance. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #1128935. 	[Title] Prognosing the Survival Time of the Patients with the Anaplastic Thyroid Carcinoma with Machine Learning [Abstract] Anaplastic thyroid carcinoma is a rare but very aggressive tumor. Many factors that might influence the survival of patients have been suggested. The aim of our study was to determine which of the factors, known at the time of admission to the hospital, might predict survival of patients with anaplastic thyroid carcinoma. Our aim was also to assess the relative importance of the factors and to identify potentially useful decision and regression trees generated by machine learning algorithms. Our study included 126 patients (90 females and 36 males; mean age was 66.7 years) with anaplastic thyroid carcinoma treated at the Institute of Oncology Ljubljana from 1972 to 1992. Patients were classified into categories according to 11 attributes: sex, age, history, physical findings, extent of disease on admission, and tumor morphology. In this paper we compare the machine learning approach with the previous statistical evaluations on the problem (uni-variate and multivariate analysis) and show that it can provide more thorough analysis and improve understanding of the data. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #63477. 	[Title] Efficient Feature Selection in Conceptual Clustering [Abstract] Feature selection has proven to be a valuable technique in supervised learning for improving predictive accuracy while reducing the number of attributes considered in a task. We investigate the potential for similar benefits in an unsupervised learning task, conceptual clustering. The issues raised in feature selection by the absence of class labels are discussed and an implementation of a sequential feature selection algorithm based on an existing conceptual clustering system is described. Additionally, we present a second implementation which employs a technique for improving the efficiency of the search for an optimal description and compare the performance of both algorithms. Please summarise all the given information.

Paper #14531 has the title `Context-Sensitive Feature Selection for Lazy Learners  
` and the abstract ` 
`.
Question: Which category should Paper #14531 be classified as ?
You can select one from ['Neural_Networks', 'Case_Based', 'Theory', 'Reinforcement_Learning', 'Probabilistic_Methods', 'Rule_Learning', 'Genetic_Algorithms'].


Paper #14531 has the title `Context-Sensitive Feature Selection for Lazy Learners  
` and the abstract ` 
`.
Question: Which category should Paper #14531 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