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An Application on Text Classification Based on Granular Computing
Machine learning is the key to text classification, a granular computing approach to machine learning is applied to learning classification rules by considering the two basic issues: concept formation and concept relationships identification. In this paper, we concentrate on the selection of a single granule in each step to construct a granule network. A classification rule induction method is proposed
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Methods of conceptual clustering and their relation to numerical taxonomy
Artificial Intelligence (AI) methods for machine learning can be viewed as forms of exploratory data analysis, even though they differ markedly from the statistical methods generally connoted by the term. The distinction between methods of machine learning and statistical data analysis is primarily due to differences in the way techniques of each type represent data and structure within data. That is, methods of machine learning are strongly biased toward symbolic (as opposed to numeric) data representations. We explore this difference within a limited context, devoting the bulk of our paper to the explication of conceptual clustering, an extension to the statistically based methods of numerical taxonomy. In conceptual clustering the formation of object clusters is dependent on the quality of 'higher-level' characterizations, termed concepts, of the clusters. The form of concepts used by existing conceptual clustering systems (sets of necessary and sufficient conditions) is described in some detail. This is followed by descriptions of several conceptual clustering techniques, along with sample output. We conclude with a discussion of how alternative concept representations might enhance the effectiveness of future conceptual clustering systems
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A survey of clustering methods
In this paper, I describe a large variety of clustering methods within a single framework. This paper unifies work across different fields, from biology (numerical taxonomy) to machine learning (concept formation). An important objective for this paper is to show that one can benefit by a knowledge of research across different disciplines. After describing the task from a set of different viewpoints or paradigms, I begin by describing the similarity measures or evaluation functions that form the basis of any clustering technique. Next, I describe a number of different algorithms that use these measures, and I close with a brief discussion of ways to evaluate different approaches to clustering
Attributing Learned Concepts in Neural Networks to Training Data
By now there is substantial evidence that deep learning models learn certain
human-interpretable features as part of their internal representations of data.
As having the right (or wrong) concepts is critical to trustworthy machine
learning systems, it is natural to ask which inputs from the model's original
training set were most important for learning a concept at a given layer. To
answer this, we combine data attribution methods with methods for probing the
concepts learned by a model. Training network and probe ensembles for two
concept datasets on a range of network layers, we use the recently developed
TRAK method for large-scale data attribution. We find some evidence for
convergence, where removing the 10,000 top attributing images for a concept and
retraining the model does not change the location of the concept in the network
nor the probing sparsity of the concept. This suggests that rather than being
highly dependent on a few specific examples, the features that inform the
development of a concept are spread in a more diffuse manner across its
exemplars, implying robustness in concept formation
Concept-based explainability for an EEG transformer model
Deep learning models are complex due to their size, structure, and inherent
randomness in training procedures. Additional complexity arises from the
selection of datasets and inductive biases. Addressing these challenges for
explainability, Kim et al. (2018) introduced Concept Activation Vectors (CAVs),
which aim to understand deep models' internal states in terms of human-aligned
concepts. These concepts correspond to directions in latent space, identified
using linear discriminants. Although this method was first applied to image
classification, it was later adapted to other domains, including natural
language processing. In this work, we attempt to apply the method to
electroencephalogram (EEG) data for explainability in Kostas et al.'s BENDR
(2021), a large-scale transformer model. A crucial part of this endeavor
involves defining the explanatory concepts and selecting relevant datasets to
ground concepts in the latent space. Our focus is on two mechanisms for EEG
concept formation: the use of externally labeled EEG datasets, and the
application of anatomically defined concepts. The former approach is a
straightforward generalization of methods used in image classification, while
the latter is novel and specific to EEG. We present evidence that both
approaches to concept formation yield valuable insights into the
representations learned by deep EEG models.Comment: To appear in proceedings of 2023 IEEE International workshop on
Machine Learning for Signal Processin
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