Incremental learning of independent, overlapping, and graded concept descriptions with an instance-based process framework

Supervised learning algorithms make several simplifying assumptions concerning the characteristics of the concept descriptions to be learned. For example, concepts are often assumed to be (1) defined with respect to the same set of relevant attributes, (2) disjoint in instance space, and (3) have uniform instance distributions. While these assumptions constrain the learning task, they unfortunately limit an algorithm's applicability. We believe that supervised learning algorithms should learn attribute relevancies independently for each concept, allow instances to be members of any subset of concepts, and represent graded concept descriptions. This paper introduces a process framework for instance-based learning algorithms that exploit only specific instance and performance feedback information to guide their concept learning processes. We also introduce Bloom, a specific instantiation of this framework. Bloom is a supervised, incremental, instance-based learning algorithm that learns relative attribute relevancies independently for each concept, allows instances to be members of any subset of concepts, and represents graded concept memberships. We describe empirical evidence to support our claims that Bloom can learn independent, overlapping, and graded concept descriptions

Comparing instance-averaging with instance-saving learning algorithms

The goal of our research is to understand the power and appropriateness of instance-based representations and their associated acquisition methods. This paper concerns two methods for reducing storage requirements for instance-based learning algorithms. The first method, termed instance-saving, represents concept descriptions by selecting and storing a representative subset of the given training instances. We provide an analysis for instance-saving techniques and specify one general class of concepts that instance-saving algorithms are capable of learning. The second method, termed instance-averaging, represents concept descriptions by averaging together some training instances while simply saving others. We describe why analyses for instance-averaging algorithms are difficult to produce. Our empirical results indicate that storage requirements for these two methods are roughly equivalent. We outline the assumptions of instance-averaging algorithms and describe how their violation might degrade performance. To mitigate the effects of non-convex concepts, a dynamic thresholding technique is introduced and applied in both the averaging and non-averaging learning algorithms. Thresholding increases the storage requirements but also increases the quality of the resulting concept descriptions

Spontaneous Analogy by Piggybacking on a Perceptual System

Most computational models of analogy assume they are given a delineated source domain and often a specified target domain. These systems do not address how analogs can be isolated from large domains and spontaneously retrieved from long-term memory, a process we call spontaneous analogy. We present a system that represents relational structures as feature bags. Using this representation, our system leverages perceptual algorithms to automatically create an ontology of relational structures and to efficiently retrieve analogs for new relational structures from long-term memory. We provide a demonstration of our approach that takes a set of unsegmented stories, constructs an ontology of analogical schemas (corresponding to plot devices), and uses this ontology to efficiently find analogs within new stories, yielding significant time-savings over linear analog retrieval at a small accuracy cost.Comment: Proceedings of the 35th Meeting of the Cognitive Science Society, 201

Instance-based prediction of real-valued attributes

Instance-based representations have been applied to numerous classification tasks with a fair amount of success. These tasks predict a symbolic class based on observed attributes. This paper presents a method for predicting a numeric value based on observed attributes. We prove that if the numeric values are generated by continuous functions with bounded slope, then the predicted values are accurate approximations of the actual values. We demonstrate the utility of this approach by comparing it with standard approaches for value-prediction. The approach requires no background knowledge

Detecting and removing noisy instances from concept descriptions

Several published results show that instance-based learning algorithms record high classification accuracies and low storage requirements when applied to supervised learning tasks. However, these learning algorithms are highly sensitive to training set noise. This paper describes a simple extension of instance-based learning algorithms for detecting and removing noisy instances from concept descriptions. The extension requires evidence that saved instances be significantly good classifiers before it allows them to be used for subsequent classification tasks. We show that this extension's performance degrades more slowly in the presence of noise, improves classification accuracies, and further reduces storage requirements in several artificial and real-world databases

A study of instance-based algorithms for supervised learning tasks : mathematical, empirical, and psychological evaluations

This dissertation introduces a framework for specifying instance-based algorithms that can solve supervised learning tasks. These algorithms input a sequence of instances and yield a partial concept description, which is represented by a set of stored instances and associated information. This description can be used to predict values for subsequently presented instances. The thesis of this framework is that extensional concept descriptions and lazy generalization strategies can support efficient supervised learning behavior.The instance-based learning framework consists of three components. The pre-processor component transforms an instance into a more palatable form for the performance component, which computes the instance's similarity with a set of stored instances and yields a prediction for its target value(s). Therefore, the similarity and prediction functions impose generalizations on the stored instances to inductively derive predictions. The learning component assesses the accuracy of these prediction(s) and updates partial concept descriptions to improve their predictive accuracy.This framework is evaluated in four ways. First, its generality is evaluated by mathematically determining the classes of symbolic concepts and numeric functions that can be closely approximated by IB_1, a simple algorithm specified by this framework. Second, this framework is empirically evaluated for its ability to specify algorithms that improve IB_1's learning efficiency. Significant efficiency improvements are obtained by instance-based algorithms that reduce storage requirements, tolerate noisy data, and learn domain-specific similarity functions respectively. Alternative component definitions for these algorithms are empirically analyzed in a set of five high-level parameter studies. Third, this framework is evaluated for its ability to specify psychologically plausible process models for categorization tasks. Results from subject experiments indicate a positive correlation between a models' ability to utilize attribute correlation information and its ability to explain psychological phenomena. Finally, this framework is evaluated for its ability to explain and relate a dozen prominent instance-based learning systems. The survey shows that this framework requires only slight modifications to fit these highly diverse systems. Relationships with edited nearest neighbor algorithms, case-based reasoners, and artificial neural networks are also described

Transforming Graph Representations for Statistical Relational Learning

Relational data representations have become an increasingly important topic due to the recent proliferation of network datasets (e.g., social, biological, information networks) and a corresponding increase in the application of statistical relational learning (SRL) algorithms to these domains. In this article, we examine a range of representation issues for graph-based relational data. Since the choice of relational data representation for the nodes, links, and features can dramatically affect the capabilities of SRL algorithms, we survey approaches and opportunities for relational representation transformation designed to improve the performance of these algorithms. This leads us to introduce an intuitive taxonomy for data representation transformations in relational domains that incorporates link transformation and node transformation as symmetric representation tasks. In particular, the transformation tasks for both nodes and links include (i) predicting their existence, (ii) predicting their label or type, (iii) estimating their weight or importance, and (iv) systematically constructing their relevant features. We motivate our taxonomy through detailed examples and use it to survey and compare competing approaches for each of these tasks. We also discuss general conditions for transforming links, nodes, and features. Finally, we highlight challenges that remain to be addressed

Learning Relative Attribute Weights For Instance-Based Concept Descriptions

Nosofsky recently described an elegant instance-based model (GCM) for concept learning that defined similarity (partly) in terms of a set of attribute weights. He showed that, when given the proper parameter settings, the G C M model closely fit his human subject data on classification performance. However, no algorithm was described for learning the attribute weights. The central thesis of the GCM model is that subjects distribute their attention amiong attributes to optimize their classification and learning performance. In this paper, we introduce two comprehensive process models based on the G C M . Our first model is simply an extension of the G C M that learns relative attribute weights. The GCM's learning and representational capabilities are limited - concept descriptions are assumed to be disjoint and exhaustive. Therefore, our second model is a further extension that learns a unique set of attribute weights for each concept description. Our empirical evidence indicates that this extension outperforms the simple G C M process model when the domain includes overlapping concept descriptions with conflicting attribute relevancies