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initialization method for grammar-guided genetic programming

KNOWLEDGE-BASED SYSTEMS 2007年第2期20卷 127-133页

作者： Garcia-Arnau, M. Manrique, D. Rios, J. Rodriguez-Paton, A. Univ Politecn Madrid Dept Artificial Intelligence Madrid Spain

This paper proposes a new tree-generation algorithm for grammar-guided genetic programming that includes a parameter to control the maximum size of the trees to be generated. An important feature of this algorithm is that the initial populations generated are adequately distributed in terms of tree size and distribution within the search space. Consequently, genetic programming systems starting from the initial populations generated by the proposed method have a higher convergence speed. Two different problems have been chosen to carry out the experiments: a laboratory test involving searching for arithmetical equalities and the real-world task of breast cancer prognosis. In both problems, comparisons have been made to another five important initialization methods. (c) 2006 Elsevier B.V. All rights reserved.

关键词： grammar-guided genetic programming initialization method tree-generation algorithm breast cancer prognosis

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initialization method for grammar-guided genetic programming

Initialization method for grammar-guided genetic programming

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26th SGAI International Conference on Innovative Techniques and Applications of Artificial Intelligence

作者： Garcia-Arnau, M. Manrique, D. Rios, J. Rodriguez-Paton, A. Univ Politecn Madrid Dept Artificial Intelligence Madrid Spain

ISBN: (纸本)184628662X

关键词： grammar-guided genetic programming initialization method tree-generation algorithm breast cancer prognosis

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An Improved initialization method for Fast Learning in Long Short-Term Memory-Based Markovian Spectrum Prediction

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IEEE TRANSACTIONS ON COGNITIVE COMMUNICATIONS AND NETWORKING 2021年第3期7卷 729-738页

作者： Radhakrishnan, Niranjana Kandeepan, Sithamparanathan RMIT Univ Sch Engn Melbourne Vic 3001 Australia

The opportunistic sharing of frequency bands supported in the Dynamic Spectrum Access (DSA) paradigm resolves the spectrum scarcity issue in wireless communications. To this end, deep learning models such as Long Short-Term Memory (LSTM) are becoming a popular choice for predicting the spectrum for cognitive radio type applications. However, the computational complexity to train such models can be very high, and delays in performing spectrum prediction (even in the order of msec) can reduce spectrum utilization efficiency. Here, we propose a novel method to initialize LSTM to reduce the training time to a good extent based on prior (statistical) knowledge of the input data and hence minimize the delay in spectrum prediction. This article proposes the 'Kandeepan-Niranjana (K-N) initialization', a novel initialization methodology for an LSTM based system model. We consider the well-known Markov model based spectrum utilization data with prior knowledge of the model parameters, such as the transition probabilities, to explain our method. Our results show that initialization with the parameters we propose provides a significant improvement in the training convergence of the LSTM based model for spectrum prediction. We also observe fast training convergence when the proposed method is applied to a real spectrum dataset.

关键词： Predictive models Hidden Markov models Computational modeling Training Markov processes Data models Context modeling Spectrum prediction long short-term memory initialization method deep learning neural networks

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A generalized Lorenz system-based initialization method for deep neural networks

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APPLIED SOFT COMPUTING 2024年 167卷

作者： Jia, Bowen Guo, Zhaoxia Huang, Tao Guo, Feng Wu, Huyu Sichuan Univ Business Sch Chengdu 610065 Peoples R China Sichuan Univ Coll Comp Sci Chengdu 610065 Peoples R China James Cook Univ Coll Sci & Engn Cairns Qld 4870 Australia Natl Univ Singapore Business Sch Singapore 119245 Singapore Natl Univ Singapore Inst Operat Res & Analyt Singapore 117602 Singapore

Deep neural networks (DNNs) are a powerful tool for solving complex problems. The effectiveness of DNNs largely depends on the initialization technique used. This research develops a new initialization method for DNNs that uses chaotic sequences from the generalized Lorenz system to improve their performance. The proposed method, termed Generalized Lorenz initialization (GLI), has been compared with two established initialization methods (Kaiming and Xavier) across four different DNN architectures: Informer, Neural Basis Expansion Analysis for Interpretable Time Series, Long Short-Term Memory, and NeuRewriter. The performance of these methods has been evaluated on seven time series forecasting datasets and one combinatorial optimization dataset. Results show that the GLI method improved forecasting accuracy by up to 86.47% compared to the Kaiming method and 88.86% compared to the Xavier method across all time series datasets. For the combinatorial optimization task, the GLI method reduced computational time by up to 9.24% with the better solution quality. These indicate the superiority of the GLI method over the two representative initialization methods for different DNN architectures across different problem domains.

关键词： initialization method Generalized Lorenz system Deep neural networks Time series forecasting Combinatorial optimization

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A new initialization method for categorical data clustering

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EXPERT SYSTEMS WITH APPLICATIONS 2009年第7期36卷 10223-10228页

作者： Cao, Fuyuan Liang, Jiye Bai, Liang Shanxi Univ Sch Comp & Informat Technol Taiyuan 030006 Shanxi Peoples R China Minist Educ Res Key Lab Computat Intelligence & Chinese Informat Taiyuan 030006 Peoples R China

In clustering algorithms, choosing a subset of representative examples is very important in data set. Such "exemplars" can be found by randomly choosing an initial subset of data objects and then iteratively refining it, but this works well only if that initial choice is close to a good solution. In this paper, based on the frequency of attribute values, the average density of an object is defined. Furthermore, a novel initialization method for categorical data is proposed, in which the distance between objects and the density of the object is considered. We also apply the proposed initialization method to k-modes algorithm and fuzzy k-modes algorithm. Experimental results illustrate that the proposed initialization method is superior to random initialization method and can be applied to large data sets for its linear time complexity with respect to the number of data objects. (C) 2009 Elsevier Ltd. All rights reserved.

关键词： Density Distance initialization method Initial cluster center k-modes algorithm

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A cluster centers initialization method for clustering categorical data

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EXPERT SYSTEMS WITH APPLICATIONS 2012年第9期39卷 8022-8029页

作者： Bai, Liang Liang, Jiye Dang, Chuangyin Cao, Fuyuan Shanxi Univ Sch Comp & Informat Technol Minist Educ Key Lab Computat Intelligence & Chinese Informat Taiyuan 030006 Shanxi Peoples R China City Univ Hong Kong Dept Mfg Engn & Engn Management Hong Kong Hong Kong Peoples R China

The leading partitional clustering technique, k-modes, is one of the most computationally efficient clustering methods for categorical data. However, the performance of the k-modes clustering algorithm which converges to numerous local minima strongly depends on initial cluster centers. Currently, most methods of initialization cluster centers are mainly for numerical data. Due to lack of geometry for the categorical data, these methods used in cluster centers initialization for numerical data are not applicable to categorical data. This paper proposes a novel initialization method for categorical data which is implemented to the k-modes algorithm. The method integrates the distance and the density together to select initial cluster centers and overcomes shortcomings of the existing initialization methods for categorical data. Experimental results illustrate the proposed initialization method is effective and can be applied to large data sets for its linear time complexity with respect to the number of data objects. (C) 2012 Elsevier Ltd. All rights reserved.

关键词： The k-modes algorithm initialization method Initial cluster centers Density Distance

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An initialization method to improve the training time of matrix factorization algorithm for fast recommendation

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SOFT COMPUTING 2021年第5期25卷 3975-3987页

作者： Mohammadian, Mojtaba Forghani, Yahya Torshiz, Masood Niazi Islamic Azad Univ Mashhad Branch Mashhad Razavi Khorasan Iran

Recommendation systems are successful personalizing tools and information filtering in web. One of the most important recommendation methods is matrix factorization method. In matrix factorization method, the latent features of users and items are determined in such a way that the inner product of the latent features of a user with the latent features of an item is equal to that user's rating on that item. This model is solved using alternate optimization algorithm. The solution and the prediction error of this algorithm depend on the initial values of the latent features of users which are usually set to small random values. The purpose of this paper is to propose a fast alternate optimization algorithm for matrix factorization which converges to a good solution. To do so, firstly, we show experimentally that if the latent feature vector of each user is initialized by a vector of which elements are equal, we can also obtain a proper solution using the alternate optimization algorithm. Then, we prove that if our proposed initialization method is used, the alternate optimization algorithm for matrix factorization can be simplified using Sherman-Morrison formula. Experimental results on 5 real datasets show that the runtime of our proposed algorithm is 2-45 times less than the traditional method.

关键词： Matrix factorization Recommendation system initialization method Sherman-Morrison formula

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A new initialization method for clustering categorical data

A new initialization method for clustering categorical data

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11th Pacific-Asia Conference on Knowledge Discovery and Data Mining

作者： Wu, Shu Jiang, Qingshan Huang, Joshua Zhexue Xiamen Univ Sch Software Xiamen 361005 Peoples R China Univ Hong Kong E Business Technol Inst Hong Kong Hong Kong Peoples R China

ISBN: (纸本)9783540717003

Performance of partitional clustering algorithms which converges to numerous local minima highly depends on initial cluster centers. This paper presents an initialization method which can be implemented to partitional clustering algorithms for categorical data sets with minimizing the numerical objective function. Experimental results show that the new initialization method is more efficient and stabler than the traditional one and can be implemented to large data sets for its linear time complexity.

关键词： data mining cluster analysis partitional clustering categorical attribute initialization method

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A Novel initialization method for Semi-supervised Clustering

A Novel Initialization Method for Semi-supervised Clustering

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4th International Conference on Knowledge Science, Engineering and Management (KSEM)

作者： Dang, Yanzhong Xuan, Zhaoguo Rong, Lili Liu, Ming Dalian Univ Technol Inst Syst Engn Dalian 116024 Peoples R China

ISBN: (纸本)9783642152795

In recent years, the research of semi-supervised clustering has been paid more and more attention. For most of the semi-supervised clustering algorithms, a good initialization method can create the high-quality seeds which are helpful to improve the clustering accuracy. In the real world, there are few labeled samples but many unlabeled ones, whereas most of the existing initialization methods put the unlabeled data away for clustering which may contain some potentially useful information for clustering tasks. In this paper, we propose a novel initialization method to transfer some of the unlabeled samples into labeled ones, in which the neighbors of labeled samples are identified at first and then the known labels are propagated to the unlabeled ones. Experimental results show that the proposed initialization method can improve the performance of the semi-supervised clustering.

关键词： initialization method semi-supervised clustering similarity measure

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A Linear Least Square initialization method for 3D Pose Graph Optimization Problem

A Linear Least Square Initialization Method for 3D Pose Grap...

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IEEE International Conference on Robotics and Automation (ICRA)

作者： Nasiri, S. M. Moradi, H. Hosseini, R. Univ Tehran Sch Elect & Comp Engn Tehran Iran Inst Res Fundamental Sci IPM Sch Comp Sci Tehran Iran

ISBN: (纸本)9781538630815

Pose Graph Optimization (PGO) is an important optimization problem arising in robotics and machine vision applications like 3D reconstruction and 3D SLAM. Each node of pose graph corresponds to an orientation and a location. The PGO problem finds orientations and locations of the nodes from relative noisy observation between nodes. Recent investigations show that well-known iterative PGO solvers need good initialization to converge to good solutions. However, we observed that state-of-the-art initialization methods obtain good initialization only in low noise problems, and they fail in challenging problems having more measurement noise. Consequently, iterative methods may converge to bad solutions in high noise problems. In this paper, a new method for obtaining orientations in the PGO optimization problem is presented. Like other well-known methods the initial locations are obtained from the result of a least-squares problem. The proposed method iteratively approximates the problem around current estimation and converts it to a least-squares problem. Therefore, the method can be seen as an iterative least-squares method which is computationally efficient. Simulation results show that the proposed initialization method helps the most well-known iterative solver to obtain better optima and significantly outperform other solvers in some cases.

关键词： Pose Graph Optimization Least square 3D SLAM initialization method

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