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Term-Weighting Learning via Genetic Programming for Text Classification
[article]
2014
arXiv
pre-print
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This paper describes a novel approach to learning term-weighting schemes (TWSs) in the context of text classification. ...
We propose in this article a genetic program that aims at learning effective TWSs that can improve the performance of current schemes in text classification. ...
Genetic programming Genetic programming (GP) (Langdon and Poli, 2001 ) is an evolutionary technique which follows the reproductive cycle of other evolutionary algorithms such as genetic algorithms (see ...
arXiv:1410.0640v3
fatcat:nzxxkn7a3jgw3obcyaa2jgbyle
Mammogram classification using Extreme Learning Machine and Genetic Programming
2014
2014 International Conference on Computer Communication and Informatics
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Supervised learning algorithm Support Vector Machine (SVM) with kernels like Linear, Polynomial and Radial Basis Function and evolutionary algorithm Genetic Programming are used to train the models. ...
The performance of the models are analysed where genetic programming approach provides more accuracy compared to Support Vector Machine in the classification of breast cancer and seems to be an fast and ...
It is observed that classification implemented by Genetic Programming in this paper is more efficient than other machine learning algorithms because the commercial GP software Discipulus uses automatic ...
doi:10.1109/iccci.2014.6921724
fatcat:7tzqsbc6g5bhjm7wmyidvlgiva
Wheat Seed Classification: Utilizing Ensemble Machine Learning Approach
2022
Scientific Programming
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The results of these algorithms are compared with the ensemble approach of machine learning. ...
In the first phase, K-nearest neighbor, classification and regression tree, and Gaussian Naïve Bayes algorithms are implemented for classification. ...
e ensemble machine learning approach with bagging and hard voting is utilized to best fit the classifier. ree machine learning algorithms K-nearest neighbors classifier (KNN), classification and regression ...
doi:10.1155/2022/2626868
fatcat:pupk77tplbhw7nvey7rivzbevq
Reinforcement learning algorithms for solving classification problems
2011
2011 IEEE Symposium on Adaptive Dynamic Programming and Reinforcement Learning (ADPRL)
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We describe a new framework for applying reinforcement learning (RL) algorithms to solve classification tasks by letting an agent act on the inputs and learn value functions. ...
This paper describes how classification problems can be modeled using classification Markov decision processes and introduces the Max-Min ACLA algorithm, an extension of the novel RL algorithm called actor-critic ...
Genetic programming [18] , [19] can also be used for making complex mental representations. ...
doi:10.1109/adprl.2011.5967372
dblp:conf/adprl/WieringHPS11
fatcat:nntuagyywbclxkpawarbhcbfni
Transductive Transfer Learning in Genetic Programming for Document Classification
[chapter]
2017
Lecture Notes in Computer Science
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In order to obtain effective classifiers on this specific task, this paper proposes a Genetic Programming (GP) system using transductive transfer learning. ...
From experimental results, the proposed transductive transfer learning GP system can evolve features from source domains to effectively apply to target domains which are similar to the source domains. ...
Additionally, prior knowledge on rule-based text classification algorithms is required for setting up the GP system. ...
doi:10.1007/978-3-319-68759-9_45
fatcat:5ae76uv6mbar7jyz4elno2a2ka
Music Feature Extraction and Classification Algorithm Based on Deep Learning
2021
Scientific Programming
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The existing approach has two shortcomings as follows: ensuring the validity and accuracy of features by manually extracting features and the traditional machine learning classification approaches not ...
The experimental results show that this approach is better than traditional manual models and machine learning-based approaches. ...
For example, literature [25] adds a genetic algorithm to the Gaussian mixture model, which improves the accuracy of classification from the experimental results. ...
doi:10.1155/2021/1651560
fatcat:7v6euhjzhjdexfi5w4jhkgmpiq
Learning discriminant functions with fuzzy attributes for classification using genetic programming
2002
Expert systems with applications
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In this paper, we propose a new learning approach based on genetic programming to generate discriminant functions for classifying data. ...
An adaptable incremental learning strategy and a distance-based ®tness function are developed to improve the ef®ciency of genetic programming-based learning process. ...
The evolutionary approach includes genetic algorithm (GA) (Wang et al., 1999; Wang, Hong, & Tseng, 1998a; Wang, Hong, Tseng, & Liao, 1998b) and genetic programming (GP) (Fretas, 1997; Kishore, Patnaik ...
doi:10.1016/s0957-4174(02)00025-8
fatcat:qmzysfxafrgcxicyoo6zbigiyy
A Genetic-Programming-Based Approach for the Learning of Compact Fuzzy Rule-Based Classification Systems
[chapter]
2006
Lecture Notes in Computer Science
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In this paper, we propose a genetic-programming-based method for the learning of an FRBCS, where disjunctive normal form (DNF) rules compete and cooperate among themselves in order to obtain an understandable ...
In any inductive learning algorithm, when we deal with problems with a large number of features, the exponential growth of the fuzzy rule search space makes the learning process more difficult. ...
In this paper, we tackle the learning of FRBCSs with high interpretability by means of a genetic-programming (GP) based approach. ...
doi:10.1007/11785231_20
fatcat:qlgarblrszcvxg2qhttpvjta6y
Automated construction of evolutionary algorithm operators for the bi-objective water distribution network design problem using a genetic programming based hyper-heuristic approach
2014
Journal of Hydroinformatics
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In this paper we investigate a novel hyper-heuristic approach that uses genetic programming (GP) to evolve mutation operators for evolutionary algorithms (EAs) which are specialised for a bi-objective ...
Key words | evolutionary algorithm, genetic programming, hyper-heuristic, mutation, optimisation, water distribution network designs impossible within reasonable time. ...
For example, in classification, the evolved programs could be used to label samples and associate them with a specific class. ...
doi:10.2166/hydro.2013.226
fatcat:s6pd4vg7cjbwxdy2ejfitkugfy
GP-COACH: Genetic Programming-based learning of COmpact and ACcurate fuzzy rule-based classification systems for High-dimensional problems
2010
Information Sciences
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In this paper we propose GP-COACH, a Genetic Programming-based method for the learning of COmpact and ACcurate fuzzy rule-based classification systems for High-dimensional problems. ...
The population constitutes the rule base, so it is a genetic cooperative-competitive learning approach. ...
genetic cooperative-competitive learning approach [41] . ...
doi:10.1016/j.ins.2009.12.020
fatcat:4bc5feuvyzeqda5yoqk5uglu5i
Brain Programming is Immune to Adversarial Attacks: Towards Accurate and Robust Image Classification using Symbolic Learning
[article]
2021
arXiv
pre-print
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We tested a prevailing bag of visual words approach from computer vision, four state-of-the-art DCNN models (AlexNet, VGG, ResNet, ResNet101), and the Brain Programming (BP) algorithm. ...
These results prove BP's robustness against adversarial examples compared to DCNN and handcrafted features methods, whose performance on the art media classification was compromised with the proposed perturbations ...
We selected six models using three of the main approaches for image classification: 1) handcrafted features approach (SIFT+FV), 2) deep genetic programming approach (BP), and 3) DCNN approach (AlexNet, ...
arXiv:2103.01359v1
fatcat:7gkb6x7odbclfaj6ynh4oil55e
Learning Features for Fingerprint Classification
[chapter]
2003
Lecture Notes in Computer Science
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In this paper, we present a fingerprint classification approach based on a novel feature-learning algorithm. ...
Unlike current research for fingerprint classification that generally uses visually meaningful features, our approach is based on Genetic Programming (GP), which learns to discover composite operators ...
Genetic Programming (GP) was first proposed by Koza in [1] . ...
doi:10.1007/3-540-44887-x_38
fatcat:v5bh4sqsgva2vhb4dhe5fpurzy
Fingerprint Classification Based on Learned Features
2005
IEEE Transactions on Systems Man and Cybernetics Part C (Applications and Reviews)
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In this paper, we present a fingerprint classification approach based on a novel feature-learning algorithm. ...
Index Terms-Composite operators, feature learning, fingerprint classification, genetic programming. ...
Genetic programming, an extension of genetic algorithms, was first proposed by Koza in [13] . ...
doi:10.1109/tsmcc.2005.848167
fatcat:a6l6sko2g5b5hcdwxpdzihkq6e
Support vector machine integrated with game-theoretic approach and genetic algorithm for the detection and classification of malware
2013
2013 IEEE Globecom Workshops (GC Wkshps)
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Data mining approaches rely on machinelearning algorithms that can be classified into three different types: supervised learning [7] , unsupervised learning [8] and semi-supervised learning [9] . ...
Genetic algorithms where the best individuals survive with the probability of one are usually known as elitist genetic algorithms. ...
doi:10.1109/glocomw.2013.6824988
dblp:conf/globecom/ZolotukhinH13
fatcat:t6iukuo5c5a4rc5d3swdgwsjqq
Deriving Rules for Forecasting Air Carrier Financial Stress and Insolvency: A Genetic Algorithm Approach
2010
Journal of the Transportation Research Forum
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This research explores the use of the genetic algorithm that has the advantages of the artificial neural network but without its limitations. ...
The genetic algorithm model resulted in a set of easy to understand, if-then rules that were used to assess U.S. air carrier solvency with a 94% accuracy. ...
Genetic Algorithms and Classification Use of the GA for bankruptcy classification can involve the generation of a linear discriminant-type of function, genetic linear function (GLF), or the generation ...
doi:10.5399/osu/jtrf.46.2.1031
fatcat:q5sgrjb23nasxdpwya2dyvnqoy
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