INVESTIGATIONS INTO THE COGNITIVE ABILITIES OF ALTERNATE LEARNING CLASSIFIER SYSTEM ARCHITECTURES

The Learning Classifier System (LCS) and its descendant, XCS, are promising paradigms for machine learning design and implementation. Whereas LCS allows classifier payoff predictions to guide system performance, XCS focuses on payoff-prediction accuracy instead, allowing it to evolve optimal classifier sets in particular applications requiring rational thought. This research examines LCS and XCS performance in artificial situations with broad social/commercial parallels, created using the non-Markov Iterated Prisoner\u27s Dilemma (IPD) game-playing scenario, where the setting is sometimes asymmetric and where irrationality sometimes pays. This research systematically perturbs a conventional IPD-playing LCS-based agent until it results in a full-fledged XCS-based agent, contrasting the simulated behavior of each LCS variant in terms of a number of performance measures. The intent is to examine the XCS paradigm to understand how it better copes with a given situation (if it does) than the LCS perturbations studied.Experiment results indicate that the majority of the architectural differences do have a significant effect on the agents\u27 performance with respect to the performance measures used in this research. The results of these competitions indicate that while each architectural difference significantly affected its agent\u27s performance, no single architectural difference could be credited as causing XCS\u27s demonstrated superiority in evolving optimal populations. Instead, the data suggests that XCS\u27s ability to evolve optimal populations in the multiplexer and IPD problem domains result from the combined and synergistic effects of multiple architectural differences.In addition, it is demonstrated that XCS is able to reliably evolve the Optimal Population [O] against the TFT opponent. This result supports Kovacs\u27 Optimality Hypothesis in the IPD environment and is significant because it is the first demonstrated occurrence of this ability in an environment other than the multiplexer and Woods problem domains.It is therefore apparent that while XCS performs better than its LCS-based counterparts, its demonstrated superiority may not be attributed to a single architectural characteristic. Instead, XCS\u27s ability to evolve optimal classifier populations in the multiplexer problem domain and in the IPD problem domain studied in this research results from the combined and synergistic effects of multiple architectural differences

SMOTE for Learning from Imbalanced Data: Progress and Challenges, Marking the 15-year Anniversary

The Synthetic Minority Oversampling Technique (SMOTE) preprocessing algorithm is considered \de facto" standard in the framework of learning from imbalanced data. This is due to its simplicity in the design of the procedure, as well as its robustness when applied to di erent type of problems. Since its publication in 2002, SMOTE has proven successful in a variety of applications from several di erent domains. SMOTE has also inspired several approaches to counter the issue of class imbalance, and has also signi cantly contributed to new supervised learning paradigms, including multilabel classi cation, incremental learning, semi-supervised learning, multi-instance learning, among others. It is standard benchmark for learning from imbalanced data. It is also featured in a number of di erent software packages | from open source to commercial. In this paper, marking the fteen year anniversary of SMOTE, we re ect on the SMOTE journey, discuss the current state of a airs with SMOTE, its applications, and also identify the next set of challenges to extend SMOTE for Big Data problems.This work have been partially supported by the Spanish Ministry of Science and Technology under projects TIN2014-57251-P, TIN2015-68454-R and TIN2017-89517-P; the Project 887 BigDaP-TOOLS - Ayudas Fundaci on BBVA a Equipos de Investigaci on Cient ca 2016; and the National Science Foundation (NSF) Grant IIS-1447795

The Hilltop 4-20-1990

This document created through a generous donation of Mr. Paul Cottonhttps://dh.howard.edu/hilltop_198090/1251/thumbnail.jp

Colonist, 1887-03-03

The Colonist began on 6 March 1886, changing its name to The Newfoundland Colonist after 18 July 1891. Having printed local and international news Monday to Saturday for six years, the paper came to an abrupt end when its offices were destroyed in The Great Fire of 8 July 1892.Title variations recorded in Alternative Title, as needed

Albuquerque Morning Journal, 06-15-1912

https://digitalrepository.unm.edu/abq_mj_news/3418/thumbnail.jp

March 23, 1995

https://scholarlycommons.obu.edu/arbn_95-99/1081/thumbnail.jp

March 23, 1995

https://scholarlycommons.obu.edu/arbn_95-99/1016/thumbnail.jp

The Republican Journal: Vol. 71, No. 1 - January 05,1899

https://digitalmaine.com/rj_1899/1000/thumbnail.jp

Columbia Chronicle (10/16/2006)

Student newspaper from October 16, 2006 entitled The Columbia Chronicle. This issue is 40 pages and is listed as Volume 41, Number 7. Cover story: Will your voice be heard on election day? Editor-in-Chief: Hunter Clausshttps://digitalcommons.colum.edu/cadc_chronicle/1682/thumbnail.jp