Decision Tree Learning with Spatial Modal Logics

• URL: http://arxiv.org/abs/2109.08325v1
• Date: Fri, 17 Sep 2021 02:35:18 GMT
• Title: Decision Tree Learning with Spatial Modal Logics
• Authors: Giovanni Pagliarini (Dept. of Mathematics and Computer Science, University of Ferrara, Italy, Dept. of Mathematical, Physical and Computer Sciences, University of Parma, Italy), Guido Sciavicco (Dept. of Mathematics and Computer Science, University of Ferrara, Italy)
• Abstract summary: More-than-propositional symbolic learning methods have started to appear, in particular for time-dependent data. We present a theory of spatial decision tree learning, and describe a prototypical implementation of a spatial decision tree learning algorithm. We compare the predicting power of spatial decision trees with that of classical propositional decision trees in several versions, for a multi-class image classification problem.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Symbolic learning represents the most straightforward approach to interpretable modeling, but its applications have been hampered by a single structural design choice: the adoption of propositional logic as the underlying language. Recently, more-than-propositional symbolic learning methods have started to appear, in particular for time-dependent data. These methods exploit the expressive power of modal temporal logics in powerful learning algorithms, such as temporal decision trees, whose classification capabilities are comparable with the best non-symbolic ones, while producing models with explicit knowledge representation. With the intent of following the same approach in the case of spatial data, in this paper we: i) present a theory of spatial decision tree learning; ii) describe a prototypical implementation of a spatial decision tree learning algorithm based, and strictly extending, the classical C4.5 algorithm; and iii) perform a series of experiments in which we compare the predicting power of spatial decision trees with that of classical propositional decision trees in several versions, for a multi-class image classification problem, on publicly available datasets. Our results are encouraging, showing clear improvements in the performances from the propositional to the spatial models, which in turn show higher levels of interpretability.

Related papers

• Inherently Interpretable Tree Ensemble Learning [7.868733904112288]
  We show that when shallow decision trees are used as base learners, the ensemble learning algorithms can become inherently interpretable. An interpretation algorithm is developed that converts the tree ensemble into the functional ANOVA representation with inherent interpretability. Experiments on simulations and real-world datasets show that our proposed methods offer a better trade-off between model interpretation and predictive performance.
  arXiv  Detail & Related papers  (2024-10-24T18:58:41Z)
• Optimized Feature Generation for Tabular Data via LLMs with Decision Tree Reasoning [53.241569810013836]
  We propose a new framework based on large language models (LLMs) and decision Tree reasoning (OCTree) Our key idea is to leverage LLMs' reasoning capabilities to find good feature generation rules without manually specifying the search space. Our empirical results demonstrate that this simple framework consistently enhances the performance of various prediction models.
  arXiv  Detail & Related papers  (2024-06-12T08:31:34Z)
• Learning accurate and interpretable decision trees [27.203303726977616]
  We develop approaches to design decision tree learning algorithms given repeated access to data from the same domain. We study the sample complexity of tuning prior parameters in Bayesian decision tree learning, and extend our results to decision tree regression. We also study the interpretability of the learned decision trees and introduce a data-driven approach for optimizing the explainability versus accuracy trade-off using decision trees.
  arXiv  Detail & Related papers  (2024-05-24T20:10:10Z)
• LOGICSEG: Parsing Visual Semantics with Neural Logic Learning and Reasoning [73.98142349171552]
  LOGICSEG is a holistic visual semantic that integrates neural inductive learning and logic reasoning with both rich data and symbolic knowledge. During fuzzy logic-based continuous relaxation, logical formulae are grounded onto data and neural computational graphs, hence enabling logic-induced network training. These designs together make LOGICSEG a general and compact neural-logic machine that is readily integrated into existing segmentation models.
  arXiv  Detail & Related papers  (2023-09-24T05:43:19Z)
• A Recursive Bateson-Inspired Model for the Generation of Semantic Formal Concepts from Spatial Sensory Data [77.34726150561087]
  This paper presents a new symbolic-only method for the generation of hierarchical concept structures from complex sensory data. The approach is based on Bateson's notion of difference as the key to the genesis of an idea or a concept. The model is able to produce fairly rich yet human-readable conceptual representations without training.
  arXiv  Detail & Related papers  (2023-07-16T15:59:13Z)
• Model-Based Deep Learning: On the Intersection of Deep Learning and Optimization [101.32332941117271]
  Decision making algorithms are used in a multitude of different applications. Deep learning approaches that use highly parametric architectures tuned from data without relying on mathematical models are becoming increasingly popular. Model-based optimization and data-centric deep learning are often considered to be distinct disciplines.
  arXiv  Detail & Related papers  (2022-05-05T13:40:08Z)
• Deep Equilibrium Assisted Block Sparse Coding of Inter-dependent Signals: Application to Hyperspectral Imaging [71.57324258813675]
  A dataset of inter-dependent signals is defined as a matrix whose columns demonstrate strong dependencies. A neural network is employed to act as structure prior and reveal the underlying signal interdependencies. Deep unrolling and Deep equilibrium based algorithms are developed, forming highly interpretable and concise deep-learning-based architectures.
  arXiv  Detail & Related papers  (2022-03-29T21:00:39Z)
• SONG: Self-Organizing Neural Graphs [10.253870280561609]
  Decision trees are easy to interpret since they are based on binary decisions, they can make decisions faster, and they provide a hierarchy of classes. One of the well-known drawbacks of decision trees, as compared to decision graphs, is that decision trees cannot reuse the decision nodes. In this paper, we provide a general paradigm based on Markov processes, which allows for efficient training of the special type of decision graphs, which we call Self-Organizing Neural Graphs (SONG)
  arXiv  Detail & Related papers  (2021-07-28T07:53:53Z)
• MurTree: Optimal Classification Trees via Dynamic Programming and Search [61.817059565926336]
  We present a novel algorithm for learning optimal classification trees based on dynamic programming and search. Our approach uses only a fraction of the time required by the state-of-the-art and can handle datasets with tens of thousands of instances.
  arXiv  Detail & Related papers  (2020-07-24T17:06:55Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.