Multi-Objective Evolutionary Rule-Based Classification with Categorical Data. [PDF]
Entropy (Basel), 2018 Jiménez F +4 more
europepmc +3 more sources
Methods for Classifying Nonprofit Organizations According to their Field of Activity: A Report on Semi-automated Methods Based on Text [PDF]
, 2020 There are various methods for classifying nonprofit organizations (NPOs) according to their field of activity. We report our experiences using two semi-automated methods based on textual data: rule-based classification and machine learning with curated ...
Karner, Dominik +2 more
core +1 more source
Random Prism: An Alternative to Random Forests. [PDF]
, 2011 Ensemble learning techniques generate multiple classifiers, so called base classifiers, whose combined classification results are used in order to increase the overall classification accuracy.
Bramer, Max, Stahl, Frederic
core +1 more source
Using a unified measure function for heuristics, discretization, and rule quality evaluation in Ant-Miner [PDF]
, 2013 Ant-Miner is a classification rule discovery algorithm that is based on Ant Colony Optimization (ACO) meta-heuristic. cAnt-Miner is the extended version of the algorithm that handles continuous attributes on-the-fly during the rule construction process ...
Otero, Fernando E.B., Salama, Khalid M.
core +1 more source
An artificial immune system for fuzzy-rule induction in data mining [PDF]
, 2004 This work proposes a classification-rule discovery algorithm integrating artificial immune systems and fuzzy systems. The algorithm consists of two parts: a sequential covering procedure and a rule evolution procedure.
A.A. Freitas +11 more
core +1 more source
Interpretable multiclass classification by MDL-based rule lists [PDF]
, 2019 Interpretable classifiers have recently witnessed an increase in attention from the data mining community because they are inherently easier to understand and explain than their more complex counterparts.
Proença, Hugo M., van Leeuwen, Matthijs
core +3 more sources
Learning Interpretable Rules for Multi-label Classification
, 2018 Multi-label classification (MLC) is a supervised learning problem in which, contrary to standard multiclass classification, an instance can be associated with several class labels simultaneously.
A Gabriel +43 more
core +1 more source
Towards a Comprehensible and Accurate Credit Management Model: Application of four Computational Intelligence Methodologies [PDF]
, 2006 The paper presents methods for classification of applicants into different categories of credit risk using four different computational intelligence techniques. The selected methodologies involved in the rule-based categorization task are (1) feedforward
Ampazis , Nikolaos +2 more
core +1 more source
, 2009 Land cover classification using multispectral satellite image is a very challenging task with numerous practical applications. We propose a multi-stage classifier that involves fuzzy rule extraction from the training data and then generation of a ...
Das, J., Laha, Arijit, Pal, Nikhil R.
core +1 more source
Improving the Interpretability of Classification Rules Discovered by an Ant Colony Algorithm: Extended Results [PDF]
, 2016 The vast majority of Ant Colony Optimization (ACO) algorithms for inducing classification rules use an ACO-based procedure to create a rule in an one-at-a-time fashion. An improved search strategy has been proposed in the cAnt-MinerPB algorithm, where an
Freitas, Alex A., Otero, Fernando E.B.
core +2 more sources