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Term-Weighting Learning via Genetic Programming for Text Classification [article]

Hugo Jair Escalante, Mauricio A. García-Limón, Alicia Morales-Reyes, Mario Graff, Manuel Montes-y-Gómez, Eduardo F. Morales
2014 arXiv   pre-print
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

K Menaka, S Karpagavalli
2014 2014 International Conference on Computer Communication and Informatics  
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

Ajay Khatri, Shweta Agrawal, Jyotir M. Chatterjee, Punit Gupta
2022 Scientific Programming  
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

Marco A. Wiering, Hado van Hasselt, Auke-Dirk Pietersma, Lambert Schomaker
2011 2011 IEEE Symposium on Adaptive Dynamic Programming and Reinforcement Learning (ADPRL)  
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]

Wenlong Fu, Bing Xue, Mengjie Zhang, Xiaoying Gao
2017 Lecture Notes in Computer Science  
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

Jingwen Zhang, Shah Nazir
2021 Scientific Programming  
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

Been-Chian Chien, Jung Yi Lin, Tzung-Pei Hong
2002 Expert systems with applications  
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]

F. J. Berlanga, M. J. del Jesus, M. J. Gacto, F. Herrera
2006 Lecture Notes in Computer Science  
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

Kent McClymont, Edward C. Keedwell, Dragan Savić, Mark Randall-Smith
2014 Journal of Hydroinformatics  
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

F.J. Berlanga, A.J. Rivera, M.J. del Jesus, F. Herrera
2010 Information Sciences  
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]

Gerardo Ibarra-Vazquez, Gustavo Olague, Mariana Chan-Ley, Cesar Puente, Carlos Soubervielle-Montalvo
2021 arXiv   pre-print
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]

Xuejun Tan, Bir Bhanu, Yingqiang Lin
2003 Lecture Notes in Computer Science  
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

X. Tan, B. Bhanu, Y. Lin
2005 IEEE Transactions on Systems Man and Cybernetics Part C (Applications and Reviews)  
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

Mikhail Zolotukhin, Timo Hamalainen
2013 2013 IEEE Globecom Workshops (GC Wkshps)  
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

Sergio Davalos, Richard Gritta, Bahram Adrangi
2010 Journal of the Transportation Research Forum  
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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