Instance-based Reasoning Research Articles

Concepts of neighbors and their application to instance-based learning on relational data

Knowledge graphs and other forms of relational data have become a widespread kind of data, and powerful methods to analyze and learn from them are needed. Formal Concept Analysis (FCA) is a mathematical framework for the analysis of symbolic datasets, which has been extended to graphs and relational data, like Graph-FCA. It encompasses various tasks such as pattern mining or machine learning, but its application generally relies on the computation of a concept lattice whose size can be exponential with the number of instances. We propose to follow an instance-based approach where the learning effort is delayed until a new instance comes in, and an inference task is set. This is the approach adopted in k-Nearest Neighbors, and this relies on a distance between instances. We define a conceptual distance based on FCA concepts, and from there the notion of concepts of neighbors, which can be used as a basis for instance-based reasoning. Those definitions are given for both classical FCA and Graph-FCA. We provide efficient algorithms for computing concepts of neighbors, and we demonstrate their inference capabilities by presenting three different applications: query relaxation, knowledge graph completion, and relation extraction.

Robust and explainable identification of logical fallacies in natural language arguments

The spread of misinformation, propaganda, and flawed argumentation has been amplified in the Internet era. Given the volume of data and the subtlety of identifying violations of argumentation norms, supporting information analytics tasks, like content moderation, with trustworthy methods that can identify logical fallacies is essential. In this paper, we formalize prior theoretical work on logical fallacies into a comprehensive three-stage evaluation framework of detection, coarse-grained, and fine-grained classification. We adapt existing evaluation datasets for each stage of the evaluation. We employ three families of robust and explainable methods based on prototype reasoning, instance-based reasoning, and knowledge injection. The methods combine language models with background knowledge and explainable mechanisms. Moreover, we address data sparsity with strategies for data augmentation and curriculum learning. Our three-stage framework natively consolidates prior datasets and methods from existing tasks, like propaganda detection, serving as an overarching evaluation testbed. We extensively evaluate these methods on our datasets, focusing on their robustness and explainability. Our results provide insight into the strengths and weaknesses of the methods on different components and fallacy classes, indicating that fallacy identification is a challenging task that may require specialized forms of reasoning to capture various classes. We share our open-source code and data on GitHub to support further work on logical fallacy identification.

Color Restoration Algorithm Based on Color Instance Under Low Illumination

Color restoration algorithm using color instance-based reasoning is proposed. The proposed system does not employ a physical model but color instance. The color instance, Color Change Vector, is defined based on distribution of color values for 175 color scheme cards taken under low and standard illuminations. Modification of CCV is proposed based on color value and linguistic expression. The given image is restored by adding the appropriate color instances for a given color value. A prototype system for color restoration is constructed. An experiment is done with dynamic images of an outdoor walking person, to evaluate the performance of the proposed system in terms of color-difference and processing time. The proposed method presents a foundation for identifying a person captured by a practical security system using a low cost CCD camera.

SpamHunting: An instance-based reasoning system for spam labelling and filtering

In this paper we show an instance-based reasoning e-mail filtering model that outperforms classical machine learning techniques and other successful lazy learners approaches in the domain of anti-spam filtering. The architecture of the learning-based anti-spam filter is based on a tuneable enhanced instance retrieval network able to accurately generalize e-mail representations. The reuse of similar messages is carried out by a simple unanimous voting mechanism to determine whether the target case is spam or not. Previous to the final response of the system, the revision stage is only performed when the assigned class is spam whereby the system employs general knowledge in the form of meta-rules.

Spatial inference with geometric proportional analogies

We describe an instance-based reasoning solution to a variety of spatial reasoning problems. The solution centers on identifying an isomorphic mapping between labelled graphs that represent some problem data and a known solution instance. We describe a number of spatial reasoning problems that are solved by generating non-deductive inferences, integrating topology with area (and other) features. We report the accuracy of our algorithm on different categories of spatial reasoning tasks from the domain of Geographical Information Science. The generality of our approach is illustrated by also solving geometric proportional (IQ-test type) analogy problems.

Applying lazy learning algorithms to tackle concept drift in spam filtering

A great amount of machine learning techniques have been applied to problems where data is collected over an extended period of time. However, the disadvantage with many real-world applications is that the distribution underlying the data is likely to change over time. In these situations, a problem that many global eager learners face is their inability to adapt to local concept drift. Concept drift in spam is particularly difficult as the spammers actively change the nature of their messages to elude spam filters. Algorithms that track concept drift must be able to identify a change in the target concept (spam or legitimate e-mails) without direct knowledge of the underlying shift in distribution. In this paper we show how a previously successful instance-based reasoning e-mail filtering model can be improved in order to better track concept drift in spam domain. Our proposal is based on the definition of two complementary techniques able to select both terms and e-mails representative of the current situation. The enhanced system is evaluated against other well-known successful lazy learning approaches in two scenarios, all within a cost-sensitive framework. The results obtained from the experiments carried out are very promising and back up the idea that instance-based reasoning systems can offer a number of advantages tackling concept drift in dynamic problems, as in the case of the anti-spam filtering domain.

Color Instance-Based Reasoning and its Application to Dynamic Image Restoration Under Low Luminance Conditions

Color instance-based reasoning is applied to dynamic image restoration under low luminance conditions is presented by introducing color change vectors. The color change vectors are constructed based on image measurement, i.e., no optical lighting models are required. This property enables us to deal with low luminance conditions, under which it is difficult to capture color information using traditional optics-based methods. The proposed algorithm restores color values by adaptively modifying the color change vectors in response to a given dynamic image. Experiments are done with still and dynamic images to compare the proposed algorithm with the conventional one in terms of color difference. The experimental results show that the proposed algorithm decreases the color-difference a maximum of 20% compared to the conventional algorithm. The proposed algorithm presents the foundation to identify a person by a low cost CCD camera in the practical security system.

IBR retrieval method based on topology preserving mappings

Case-based reasoning systems, in general, and instance-based reasoning systems, in particular, are used more and more in industrial applications nowadays. During the last few years, researchers have been working in the development of techniques to automate the reasoning stages identified in this methodology. This paper presents a method for automating the retrieval stage and indexation of instance-based reasoning systems. This method is based on a modification of a new type of topology preserving map that can be used for scale invariant classification. The scale invariant map is an implementation of the negative feedback network to form a topology-preserving mapping. Maximum/minimum likelihood learning is applied in this paper to the scale invariant map and its possibilities are explored. This method automates the organization of cases and the retrieval stage of case-based reasoning systems. The proposed methodology groups instances with similar structure, identifying clusters automatically in a data set in an unsupervised mode. The method has been successfully used to completely automate the reasoning process of an oceanographic forecasting system and to improve its performance.

Fuzzy set-based methods in instance-based reasoning

A formal framework of instance-based prediction is presented in which the generalization beyond experience is founded on the concepts of similarity and possibility. The underlying extrapolation principle is formalized within the framework of fuzzy rules. Thus, instance-based reasoning can be realized as fuzzy set-based approximate reasoning. More precisely, our model makes use of so-called possibility rules. These rules establish a relation between the concepts of similarity and possibility, which takes the uncertain character of similarity-based inference into account: inductive inference is possibilistic in the sense that predictions take the form of possibility distributions on the set of outcomes, rather than precise (deterministic) estimations. The basic model is extended by means of fuzzy set-based modeling techniques. This extension provides the basis for incorporating domain-specific (expert) knowledge. Thus, our approach favors a view of instance-based reasoning according to which the user interacts closely with the system.

Model adaptation in possibilistic instance-based reasoning

This paper extends the possibilistic approach to instance-based reasoning that has recently been developed in a companion paper. Within the framework of this approach, the similarity-guided extrapolation principle underlying instance-based learning is formalized by means of so-called possibility rules, a special type of fuzzy rules. Proceeding from this idea, a methodology has been outlined, which allows a human expert to specify a model of the inference mechanism in a linguistic way. In this paper, a method for adapting a linguistic model automatically to observed data is proposed. This extension frees the expert from specifying mathematical concepts such as similarity measures and membership functions of fuzzy sets precisely. Rather, the expert determines only the qualitative structure of the model, which is then "calibrated" bit using the cases stored in memory.

HYBRID INSTANCE-BASED SYSTEM FOR PREDICTING OCEAN TEMPERATURES

An instance-based problem solving model is presented in which the aim is to forecast, in real time, the physical parameter values of a complex and dynamic environment: the ocean. The situations in which the rules that determine a system are unknown, the prediction of the parameter values that determine the characteristic behaviour of the system can be a problematic task. In such a situation it has been found that an instance-based reasoning system can provide a more effective means of performing such predictions than other connectionist or symbolic techniques. The instance-based reasoning system incorporates a radial basis function artificial neural network for the instance adaptation. The results obtained from experiments, in which the system operated in real time in the oceanographic environment, are presented.

Automating the construction of CBR systems using kernel methods

Instance-based reasoning systems and, in general, case-based reasoning systems are normally used in problems for which it is difficult to define rules. Although case-based reasoning methods have proved their ability to solve different types of problems, there is still a demand for methods that facilitate their automation during their creation and the retrieval and reuse stages of their reasoning circle. This paper presents one method based on kernels, which can be used to automate some of the reasoning steps of instance-based reasoning systems. Kernels were originally derived in the context of support vector machines, which identify the smallest number of data points necessary to solve a particular problem (e.g., regression or classification). Unsupervised kernel methods have been used successfully to identify the optimal instances to instantiate an instance base. The efficiency of the kernel model is shown on an oceanographic problem. © 2001 John Wiley & Sons, Inc.

Learning to Understand the Balance Beam

Twenty-two university students who did not initially know the quantitative rule for predicting whether a configuration of weights placed on a balance beam would cause the beam to balance, tip left, or tip right were asked to induce the rule in a training procedure adapted from Siegler (1976). For each of a series of balance beam problems, subjects predicted the action of the beam and explained how they arrived at their prediction. Protocols revealed that although all subjects realized on early on that both weight and distance were relevant to their predictions, they used a variety of heuristics prior to inducing the correct quantitative rule. These heuristics included instance-based reasoning, qualitative estimation of distance, and the use of quantitative rules of limited generality. The common use of instance-based reasoning suggests that learning to understand the balance beam cannot be described completely in terms of a simple rule acquisition theory. Also, the variability in the use of heuristics across subjects suggests that no simple theory that depicts subjects as linearly progressing through a hierarchy of levels can be adequately describe the development of balance understanding.