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Directly optimizing evaluation measures in learning to rank based on the clonal selection algorithm

Qiang He, Jun Ma, Shuaiqiang Wang
2010 Proceedings of the 19th ACM international conference on Information and knowledge management - CIKM '10  
It employs the clonal selection algorithm to learn an effective ranking function by combining various evidences in IR.  ...  One fundamental issue of learning to rank is the choice of loss function to be optimized.  ...  RankCSA employs the clonal selection algorithm to learn an effective ranking function by combining various evidences in IR. Our method avoids optimizing surrogate loss functions of IR measures.  ... 
doi:10.1145/1871437.1871644 dblp:conf/cikm/HeMW10 fatcat:rxc4nv5lgfa4xmxtplqzl3juge

Research on B Cell Algorithm for Learning to Rank Method Based on Parallel Strategy

Yuling Tian, Hongxian Zhang, Quan Zou
2016 PLoS ONE  
This paper proposes a parallel B cell algorithm, RankBCA, for rank learning which utilizes a clonal selection mechanism based on biological immunity.  ...  The novel algorithm is compared with traditional rank-learning algorithms through experimentation and shown to outperform the others in respect to accuracy, learning time, and convergence rate; taken together  ...  Analyzed the data: YT. Contributed reagents/materials/analysis tools: YT HZ. Wrote the paper: YT HZ.  ... 
doi:10.1371/journal.pone.0157994 pmid:27487242 pmcid:PMC4972358 fatcat:nbxpmvhbcrbpbawm6435w2czfq

Clonal selection based genetic algorithm for workflow service selection

Simone A. Ludwig
2012 2012 IEEE Congress on Evolutionary Computation  
The selection based on QoS allows the user to include also non-functional attributes in their query, such as availability and reliability.  ...  Experimental results show that the clonal selection based genetic algorithm achieves much higher fitness values for the workflow selection problem than standard genetic algorithm.  ...  ACKNOWLEDGMENT This paper is partly based on research supported by North Dakota EPSCoR and National Science Foundation Grant EPS-0814442.  ... 
doi:10.1109/cec.2012.6256465 dblp:conf/cec/Ludwig12 fatcat:7bv2pybp7jg4znpzpyslzjol3u

Learning to rank using evolutionary computation

Shuaiqiang Wang, Jun Ma, Jiming Liu
2009 Proceeding of the 18th ACM conference on Information and knowledge management - CIKM '09  
Inspired by the GP based learning to rank approaches, we provide a series of generalized definitions and a common framework for the application of EC in learning to rank research.  ...  Nowadays ranking function discovery approaches using Evolutionary Computation (EC), especially Genetic Programming (GP), have become an important branch in the Learning to Rank for Information Retrieval  ...  By contrary, EC based approaches can optimize solutions by evaluating these IR performance measures directly in order to obtain a good ranking function.  ... 
doi:10.1145/1645953.1646254 dblp:conf/cikm/WangML09 fatcat:23wvzm4tibbojbpmmvvwf2wsam

Adaboost Classifier by Artificial Immune System Model [chapter]

Hind Taud, Juan Carlos Herrera-Lozada, Jesús Álvarez-Cedillo
2010 Lecture Notes in Computer Science  
In Adaboost, through learning, the search for the best simple classifiers is replaced by the clonal selection algorithm. Haar features extracted from face database are chosen as a case study.  ...  An algorithm combining Artificial Immune System and AdaBoost called Imaboost is proposed to improve the feature selection and classification performance.  ...  Acknowledgments We thank Anna Reid for improving the English grammar and style of our manuscript.  ... 
doi:10.1007/978-3-642-15992-3_19 fatcat:no5exxqghvh7vebvygyjlw6cki

A cross-benchmark comparison of 87 learning to rank methods

Niek Tax, Sander Bockting, Djoerd Hiemstra
2015 Information Processing & Management  
In this paper we propose a way to compare learning to rank methods based on a sparse set of evaluation results on a set of benchmark datasets.  ...  However, comparison of learning to rank methods based on evaluation results is hindered by nonexistence of a standard set of evaluation benchmark collections.  ...  Directly optimizing evaluation measures in learning to rank based on the clonal selection algorithm.  ... 
doi:10.1016/j.ipm.2015.07.002 fatcat:vityxuoyxzfezhdizq7sfofwka

Weka Machine Learning for Predicting the Phospholipidosis Inducing Potential

Ovidiu Ivanciuc
2008 Current Topics in Medicinal Chemistry  
Here we present structure-activity relationships (SAR) computed with a wide variety of machine learning algorithms trained to identify drugs that have phospholipidosis inducing potential.  ...  All SAR models are developed with the machine learning software Weka, and include both classical algorithms, such as k-nearest neighbors and decision trees, as well as recently introduced methods, such  ...  Examples of AIS algorithms used in optimization, pattern recognition, or machine learning are the clonal selection algorithm (CLONALG) [84, 85] , the clonal selection classification system (CSCA) [86  ... 
doi:10.2174/156802608786786589 pmid:19075775 fatcat:6ytu27uwrzbg3nruc7o6bnoxra

Enhancing the learning capacity of immunological algorithms: a comprehensive study of learning operators

Shangce Gao, Tao Gong, Weiya Zhong, Fang Wang, Beibei Chen
2013 Advances in Artificial Life, ECAL 2013  
Experiments are conducted based on nine variants of immunological algorithms that use different learning operators.  ...  The problem-solving performance of immunological algorithms mainly lies on the utilization of learning (i.e. mutation) operators.  ...  Acknowledgements This work is partially supported by the National Natural Science Foundation of China under Grants 61203325, 61271114 and 61003205, Genguang Project of Shanghai E-  ... 
doi:10.7551/978-0-262-31709-2-ch130 dblp:conf/ecal/GaoGZWC13 fatcat:eds5vusyq5e5vpaubxu76cbeoy

A Cooperative Coevolution Framework for Parallel Learning to Rank

Shuaiqiang Wang, Yun Wu, Byron J. Gao, Ke Wang, Hady W. Lauw, Jun Ma
2015 IEEE Transactions on Knowledge and Data Engineering  
With CCRank, we investigate parallel CC in the context of learning to rank. We implement CCRank with three EA-based learning to rank algorithms for demonstration.  ...  We propose CCRank, the first parallel framework for learning to rank based on evolutionary algorithms (EA), aiming to significantly improve learning efficiency while maintaining accuracy.  ...  A primary version of this paper was presented in the 25th AAAI Conference on Artificial Intelligence (AAAI), San Francisco, USA, August 7-11, 2011.  ... 
doi:10.1109/tkde.2015.2453952 fatcat:gltmjpyaencx5dnulatxppwyyy

A Multi-Learning Immune Algorithm for Numerical Optimization

Shuaiqun WANG, Shangce GAO, Aorigele, Yuki TODO, Zheng TANG
2015 IEICE Transactions on Fundamentals of Electronics Communications and Computer Sciences  
In this paper, we propose a novel multi-learning operator into the AIS to further enrich the search dynamics of the algorithm.  ...  To verify its performance, the proposed algorithm, which is called multi-learning immune algorithm (MLIA), is applied on a number of benchmark functions.  ...  Acknowledgment The authors would like to thank the Associate Editor and the anonymous reviewers for their great comments. This  ... 
doi:10.1587/transfun.e98.a.362 fatcat:2wn2zjv32vdehenmqqnc5e3rgq

Ensemble Swarm based Feature Selection (ESFS) and Ensemble Three Classifiers (ETCS) to Predict Student's Academic Performance

2019 International Journal of Engineering and Advanced Technology  
In this proposed work, Ensemble Swarm based Feature Selection (ESFS) and Ensemble Three Classifiers (ETCs) is formulated to classify the performance of students based on the selected features.  ...  ESFS algorithm fuses the Fuzzy Membership Genetic Algorithm (FMGA) and Improved Clonal Selection Algorithms (ICSAs).  ...  Filtering method is based on the characteristics of training data. This step is carried out in the preprocessing phase and is independent on the learning algorithm.  ... 
doi:10.35940/ijeat.f1096.0886s19 fatcat:i2t4uourubhuzcz6cstvlcqo7i

Development of Smart Technology for Complex Objects Prediction and Control on the Basis of a Distributed Control System and an Artificial Immune Systems Approach

Samigulina Galina, Samigulina Zarina
2019 Advances in Science, Technology and Engineering Systems  
The article describes how on the basis of the multialgorithm approach there was developed a modified algorithm based on modern artificial intelligence methods in order to select informative features (principal  ...  component method, Random Forest algorithm, particle swarm algorithm) and artificial immune systems (clonal selection) solving the image recognition problem and predicting the state of a complex control  ...  control systems based on artificial intelligence approaches" (2018-2020).  ... 
doi:10.25046/aj040312 fatcat:ts7nrpcmovb4diom5qytlxj2jq

An immune programming-based ranking function discovery approach for effective information retrieval

Shuaiqiang Wang, Jun Ma, Qiang He
2010 Expert systems with applications  
IP is a novel evolution based machine learning algorithm with the principles of immune systems, which is verified to be superior to Genetic Programming (GP) on the convergence of algorithm according to  ...  Besides, two formulae focusing on selecting best antibody for test are designed for learning to rank.  ...  Acknowledgments Thanks are given to anonymous referees for the helpful suggestions and comments that they provided.  ... 
doi:10.1016/j.eswa.2010.02.019 fatcat:4j46fqbpo5aihahivzbhodz7vi

Clonal plasticity: an autonomic mechanism for multi-agent systems to self-diversify

Vivek Nallur, Siobhán Clarke
2017 Autonomous Agents and Multi-Agent Systems  
For more information, please see the item record link above. Abstract Diversity has long been used as a design tactic in computer systems to achieve various properties.  ...  Multi-agent systems, in particular, have utilized diversity to achieve aggregate properties such as efficiency of resource allocations, and fairness in these allocations.  ...  Reinforcement-Learning(RL) is a class of machine-learning algorithms where an agent tries to learn the optimal action to take, in a certain environment, based on a succession of action-reward pairs.  ... 
doi:10.1007/s10458-017-9380-x fatcat:dbd46z3u6fdtndxe44bhggmqrm

Multiobjective evolutionary algorithms: A survey of the state of the art

Aimin Zhou, Bo-Yang Qu, Hui Li, Shi-Zheng Zhao, Ponnuthurai Nagaratnam Suganthan, Qingfu Zhang
2011 Swarm and Evolutionary Computation  
By evolving a population of solutions, multiobjective evolutionary algorithms (MOEAs) are able to approximate the Pareto optimal set in a single run.  ...  MOEAs have attracted a lot of research effort during the last 20 years, and they are still one of the hottest research areas in the field of evolutionary computation.  ...  In [69] , a hybrid immune multiobjective optimization algorithm based on a clonal selection principle was proposed.  ... 
doi:10.1016/j.swevo.2011.03.001 fatcat:jfcghitjp5ap5he4d3ackhhjsu
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