Classification of cases of animal abuse in Ecuador using IndetermSoft and C4.5 algorithms

Abstract

This dissertation aimed to determine the classification of animal abuse within Ecuador by severity, context, and solution through the application of IndetermSoft and the statistical-neutrosophic C4.5 algorithm to classify the uncertainty associated with judicial and administrative efforts for animal protection for domestic dogs and cats. The approach was qualitative and documentary, analyzing judicial sentences, citizen complaints, and municipal ordinances within the three largest Ecuadorian cities of Quito, Guayaquil, and Cuenca to acquire the determination of classifications of animal abuse, subsequent sanctions, and follow-ups. A modeling of IndetermSoft ensembles assessed the indeterminacy of subsequent actions, and inputted into IndetermSoft and the C4.5 algorithm processed to generate neutrosophic decision forests to elucidate trends and rankings of administrative and judicial actions. Ultimately, the results concluded that determining the classification of animal abuse is an effective endeavor—exacerbated by the inclusion of uncertainty—to solidify potential next steps for animal welfare in Ecuador. However, standardization of results among the municipalities, increased inter-institutional cooperation, and an alignment between national and local laws would render the application of sanctions more appropriate, which would better supplement aggression's outcomes and Ecuador's animal welfare law in a more equitable, efficient, and effective manner for animal rights.