3 research outputs found
Context-aware feature attribution through argumentation
Feature attribution is a fundamental task in both machine learning and data
analysis, which involves determining the contribution of individual features or
variables to a model's output. This process helps identify the most important
features for predicting an outcome. The history of feature attribution methods
can be traced back to General Additive Models (GAMs), which extend linear
regression models by incorporating non-linear relationships between dependent
and independent variables. In recent years, gradient-based methods and
surrogate models have been applied to unravel complex Artificial Intelligence
(AI) systems, but these methods have limitations. GAMs tend to achieve lower
accuracy, gradient-based methods can be difficult to interpret, and surrogate
models often suffer from stability and fidelity issues. Furthermore, most
existing methods do not consider users' contexts, which can significantly
influence their preferences. To address these limitations and advance the
current state-of-the-art, we define a novel feature attribution framework
called Context-Aware Feature Attribution Through Argumentation (CA-FATA). Our
framework harnesses the power of argumentation by treating each feature as an
argument that can either support, attack or neutralize a prediction.
Additionally, CA-FATA formulates feature attribution as an argumentation
procedure, and each computation has explicit semantics, which makes it
inherently interpretable. CA-FATA also easily integrates side information, such
as users' contexts, resulting in more accurate predictions
Similarity notions in bipolar abstract argumentation
The notion of similarity has been studied in many areas of Computer Science; in a general sense, this concept is defined to provide a measure of the semantic equivalence between two pieces of knowledge, expressing how 'close' their meaning can be regarded. In this work, we study similarity as a tool useful to improve the representation of arguments, the interpretation of the relations between arguments, and the semantic evaluation associated with the arguments in the argumentative process. In this direction, we present a novel mechanism to determine the similarity between two arguments based on descriptors representing particular aspects associated with these arguments. This mechanism involves a comparison process influenced by the context in which the process develops, where this context provides the relevant aspects that need to be analyzed in the application domain. Then, we use this similarity measure as a quantity to compute the result of attacks and supports in the argumentation process. These valuations, applied to a Bipolar Argumentation Frameworks, allowed us to refine the argument relations, providing the tools to establish a family of new argumentation semantics that considers the similarity between arguments as a crucial part for the argumentation process.Fil: Budán, Paola Daniela. Universidad Nacional de Santiago del Estero. Facultad de Ciencias Exactas y TecnologÃas; Argentina. Universidad Nacional del Sur; ArgentinaFil: Escañuela Gonzalez, Melisa Gisselle. Universidad Nacional de Santiago del Estero. Facultad de Ciencias Exactas y TecnologÃas; Argentina. Consejo Nacional de Investigaciones CientÃficas y Técnicas; ArgentinaFil: Budan, Maximiliano Celmo David. Consejo Nacional de Investigaciones CientÃficas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Instituto de Investigación en Ciencias de la Computación. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Instituto de Investigación en Ciencias de la Computación; Argentina. Universidad Nacional de Santiago del Estero. Facultad de Ciencias Exactas y TecnologÃas; ArgentinaFil: Martinez, Maria Vanina. Consejo Nacional de Investigaciones CientÃficas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Instituto de Investigación en Ciencias de la Computación. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Instituto de Investigación en Ciencias de la Computación; ArgentinaFil: Simari, Guillermo Ricardo. Consejo Nacional de Investigaciones CientÃficas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Instituto de Investigación en Ciencias de la Computación. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Instituto de Investigación en Ciencias de la Computación; Argentina. Universidad Nacional del Sur; Argentin