Boolean Function Learning With A Classifier System

Aim and Objective: Cancer is one of the deadliest diseases, taking the lives of millions every year. Traditional methods of treating cancer are expensive and toxic to normal cells. Fortunately, anti-cancer peptides (ACPs) can eliminate this side effect. However, the identification and development of new anti Materials and Methods: In our study, a multi-classifier system was used, combined with multiple machine learning models, to predict anti-cancer peptides. These individual learners are composed of different feature information and algorithms, and form a multi-classifier system by voting. Results and Conclusion: The experiments show that the overall prediction rate of each individual learner is above 80% and the overall accuracy of multi-classifier system for anti-cancer peptides prediction can reach 95.93%, which is better than the existing prediction model.

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Misclassification detection based on conditional VAE for rule evolution in learning classifier system

Proceedings of the Genetic and Evolutionary Computation Conference Companion ◽

10.1145/3449726.3459508 ◽

2021 ◽

Author(s):

Hiroki Shiraishi ◽

Masakazu Tadokoro ◽

Yohei Hayamizu ◽

Yukiko Fukumoto ◽

Hiroyuki Sato ◽

...

Keyword(s):

Learning Classifier System ◽

Classifier System ◽

Learning Classifier

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Separation results for boolean function classes

Cryptography and Communications ◽

10.1007/s12095-021-00488-w ◽

2021 ◽

Author(s):

Aniruddha Biswas ◽

Palash Sarkar

Keyword(s):

Boolean Function ◽

Function Classes ◽

Separation Results

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Comparison of Quantitative Morphology of Layered and Arbitrary Patterns: Contrary to Visual Perception, Binary Arbitrary Patterns Are Layered from a Structural Point of View

Applied Sciences ◽

10.3390/app11146300 ◽

2021 ◽

Vol 11 (14) ◽

pp. 6300

Author(s):

Igor Smolyar ◽

Daniel Smolyar

Keyword(s):

Boolean Function ◽

Layer Structure ◽

Central Element ◽

Morphological Characteristics ◽

Building Blocks ◽

Point Of View ◽

Living Systems ◽

Seismic Profiles ◽

Contour Maps ◽

Anisotropic Layers

Patterns found among both living systems, such as fish scales, bones, and tree rings, and non-living systems, such as terrestrial and extraterrestrial dunes, microstructures of alloys, and geological seismic profiles, are comprised of anisotropic layers of different thicknesses and lengths. These layered patterns form a record of internal and external factors that regulate pattern formation in their various systems, making it potentially possible to recognize events in the formation history of these systems. In our previous work, we developed an empirical model (EM) of anisotropic layered patterns using an N-partite graph, denoted as G(N), and a Boolean function to formalize the layer structure. The concept of isotropic and anisotropic layers was presented and described in terms of the G(N) and Boolean function. The central element of the present work is the justification that arbitrary binary patterns are made up of such layers. It has been shown that within the frame of the proposed model, it is the isotropic and anisotropic layers themselves that are the building blocks of binary layered and arbitrary patterns; pixels play no role. This is why the EM can be used to describe the morphological characteristics of such patterns. We present the parameters disorder of layer structure, disorder of layer size, and pattern complexity to describe the degree of deviation of the structure and size of an arbitrary anisotropic pattern being studied from the structure and size of a layered isotropic analog. Experiments with arbitrary patterns, such as regular geometric figures, convex and concave polygons, contour maps, the shape of island coastlines, river meanders, historic texts, and artistic drawings are presented to illustrate the spectrum of problems that it may be possible to solve by applying the EM. The differences and similarities between the proposed and existing morphological characteristics of patterns has been discussed, as well as the pros and cons of the suggested method.

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The gender system of Longuda

Language Typology and Universals ◽

10.1515/stuf-2021-1035 ◽

2021 ◽

Vol 74 (2) ◽

pp. 327-346

Author(s):

Julius-Maximilian Elstermann ◽

Ines Fiedler ◽

Tom Güldemann

Keyword(s):

Historical Reconstruction ◽

Classifier System ◽

Class System ◽

Nominal Form ◽

Noun Class ◽

Nominal Classification ◽

Gender System

Abstract This article describes the gender system of Longuda. Longuda class marking is alliterative and does not distinguish between nominal form and agreement marking. While it thus appears to be a prototypical example of a traditional Niger-Congo “noun-class” system, this identity of gender encoding makes it look morpho-syntactic rather than lexical. This points to a formerly independent status of the exponents of nominal classification, which is similar to a classifier system and thus less canonical. Both types of class marking hosts involve two formally and functionally differing allomorphs, which inform the historical reconstruction of Longuda noun classification in various ways.

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