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共 213 条记录，以下是21-30 订阅

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Drug design by machine learning: Ensemble learning for QSAR modeling

Drug design by machine learning: Ensemble learning for QSAR ...

引用

4th international conference on machine learning and applications

作者： Liu, Y Univ Texas Dept Comp Sci Richardson TX 75083 USA

ISBN: (纸本)0769524958

A common step in drug design is the formation of a quantitative structure-activity relationship (QSAR) to model an exploratory series of compounds. A QSAR generalizes how the structure of a compound relates to its biological activity. there is growing interest in the application of machine learning techniques in QSAR modeling research. However, no single technique can claim to be uniformly superior to any other. this study introduced the ensemble machine learning, a set of classifiers whose individual decisions are combined in some way (typically by weighted or unweighted voting) to improve the performance of the overall system. A comparative study was carried out of two popular ensemble learning algorithms, Bagging and AdaBoost, for QSAR modeling. Two test case problems were studied: the inhibition of Escherichia coli dihydrofolate reductase (DHFR) by pyrimidines, and the inhibition of rat/mouse tumor DHFR by triazines. It was observed that the ensemble learning algorithms, Bagging and AdaBoost, can significantly improve the performance of Decision Tree C4.5 and 1-R (p < 0.05), while Naive Bayesian and 1-Nearest Neighbor did not benefit from ensemble learning. Furthermore, in general, AdaBoost outperformed Bagging on the tested data sets.

关键词： Drug products

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Efficient construction of relational features

Efficient construction of relational features

引用

icmla 2005: 4th international conference on machine learning and applications

作者： Železný, Filip Czech Technology University in Prague Technická 2 166 27 Prague 6 Czech Republic

ISBN: (纸本)0769524958

Devising algorithms for learning from multi-relational data is currently considered an important challenge. the wealth of traditional single-relational machine learning tools, on the other hand, calls for methods of 'propositionalization', ie. conversion of multi-relational data into single-relational representations. A major stream of propositionalization algorithms is based on the construction of truth-valued features (first-order logic atom conjunctions), which capture relational properties of data and play the role of binary attributes in the resulting single-table representation. Such algorithms typically use backtrack depth first search for the syntactic construction of features complying to user's mode/type declarations. As such they incur a complexity factor exponential in the maximum allowed feature size. Here we present a polynomial-runtime alternative based on an efficient reduction between the feature construction problem on the propositional satisfiability (SAT) problem, such that the latter involves only Horn clauses and is therefore efficiently solvable. © 2005 IEEE.

关键词： learning algorithms

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Comparing machine learning classification schemes - A GIS approach

Comparing machine learning classification schemes - A GIS ap...

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icmla 2005: 4th international conference on machine learning and applications

作者： Lazar, Alina Shellito, Bradley A. Department of Computer Science and Information Systems Youngstown State University Youngstown OH 44555 Department of Geography Youngstown State University Youngstown OH 44555

ISBN: (纸本)0769524958

this project examines the effectiveness of two classification schema: Support Vector machines (SVM), and Artificial Neural Networks (NN) when applied to geographic (i.e. spatial) data. the context for this study is to examine patterns of urbanization in Mahoning County, OH in relation to several independent driving variables of urban development. these independent variables were constructed using Geographic Information Systems (CIS) and were compared to the dependent variable of the spatial locations of urban areas in Mahoning County. the classification techniques were used in conjunction with the GIS-created variables to predict the location of urban areas within Mahoning County. A comparison of the accuracy of the techniques is presented and conclusions drawn concerning which of the variables are the most influential on urban patterns in the region. Lastly, a spatial analysis of the prediction error is performed for each method. © 2005 IEEE.

关键词： learning systems

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Proceedings: icmla 2005 Fourth international conference on machine learning and applications

Proceedings: ICMLA 2005 Fourth International Conference on M...

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icmla 2005: 4th international conference on machine learning and applications

ISBN: (纸本)0769524958

the proceedings contain 60 papers. the topics discussed include: predicting software suitability using a Bayesian belief network;parallel algorithm for control chart pattern recognition;data-centric automated data mining;a Bayesian kernel for the prediction of neuron properties from binary gene profiles;new filter-based feature selection criteria for identifying differentially expressed genes;a new clustering algorithm using message passing and its applications in analyzing microarray data;iterative weighting of phylogenetic profiles increases classification accuracy;integrating knowledge-driven and data-driven approaches for the derivation of clinical prediction rules;sparse classifiers for automated heart wall motion abnormality detection;segmenting brain tumors using alignment-based features;and the application of machine learning techniques to the prediction of erectile dysfunction.

关键词： learning systems

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Effective data mining by integrating genetic algorithm into the data preprocessing phase

Effective data mining by integrating genetic algorithm into ...

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4th international conference on machine learning and applications

作者： Gopalan, J Korkmaz, E Alhajj, R Barker, K Univ Calgary Dept Comp Sci Calgary AB T2N 1N4 Canada

ISBN: (纸本)0769524958

Dividing a data set into a training set and a test set is fundamental component in the pre-processing phase of data mining (DM). Effectively, the choice of the training set is an important factor in deriving good classification rules. Traditional approach for association rules mining divides the dataset into training set and test set based on statistical methods. In this paper, we highlight the weaknesses of the existing approach and hence propose a new methodology that employs genetic algorithm (GA) in the process. In our approach, the original dataset is divided into sample and validation sets. then, GA is used to find an appropriate split of the sample set into training and test sets. We demonstrate through experiments that using the obtained training set as the input to an association rules mining algorithm generates high accuracy classification rules. the rules are tested on the validation set for accuracy. the results are very satisfactory;they demonstrate the applicability and effectiveness of our approach.

关键词： pre-processing data mining classification association genetic algorithms clustering data-splitting

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A new clustering algorithm using message passing and its applications in analyzing microarray data

A new clustering algorithm using message passing and its app...

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icmla 2005: 4th international conference on machine learning and applications

作者： Geng, Huimin Xutao, Deng Ali, Hesham Department of Pathology and Microbiology University of Nebraska Medical Center Omaha NE 68198 Colleage of Information Science and Technology University of Nebraska at Omaha Omaha NE 68182

ISBN: (纸本)0769524958

In this paper, we proposed a new clustering algorithm that employs the concept of message passing to describe parallel and spontaneous biological processes. Inspired by real-life situations in which people in large gatherings form groups by exchanging messages, Message Passing Clustering (MPC) allows data objects to communicate with each other and produces clusters in parallel, thereby making the clustering process intrinsic and improving the clustering performance. We have proved that MPC shares similarity with hierarchical clustering but offers significantly improved performance because it takes into account both local and global structure. MPC can be easily implemented in a parallel computing platform for the purpose of speed-up. To validate the MPC method, we applied MPC to microarray data from the Stanford yeast cell-cycle database. the results show that MPC gave better clustering solutions in terms of homogeneity and separation values than other clustering methods. © 2005 IEEE.

关键词： Data transfer

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Knowledge acquisition through machine learning: Minimising expert's effort

Knowledge acquisition through machine learning: Minimising e...

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icmla 2005: 4th international conference on machine learning and applications

作者： Blanco-Vega, Ricardo Hernández-Orallo, José Ramíez-Quintana, María José Dep. de Sist. Informáticos y Computación Universidad Politécnica de Valencia C. de Vera s/n 46022 Valencia Spain

ISBN: (纸本)0769524958

machine learning can be applied to solve the knowledge acquisition bottleneck in many areas where an expert makes predictions to single cases, such as diagnosis, estimation, etc. the idea is to query the expert with as many cases as possible and get their answers. With this data we train a machine learning model which mimics the expert's behaviour. this is just a simple application of a modelling technique known as "mimetism", which has many other applications. this "soft" approach to knowledge acquisition has many advantages: any machine learning technique can be used, the expert must only answer simple questions (cases) and we can combine the decisions of several experts easily. However, one problem of this approach is that we do not know in advance how many cases we will need to ask in order to get a good model which is accurate wrt. the expert's knowledge. Obviously, as more data is labelled by the expert better results are obtained. However, asking thousands of cases to the expert is usually impractical. In this paper, we analyse the behaviour of knowledge acquisition through mimetic learning according to two factors: accuracy and comprehensibility of the resulting model and we devise a method to compute the minimum number of cases that we need to ask the expert to attain a certain quality level. © 2005 IEEE.

关键词： Knowledge acquisition

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Application of Open-Source Deep Neural Networks for Object Detection in Industrial Environments 17

Application of Open-Source Deep Neural Networks for Object D...

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17th IEEE international conference on machine learning and applications (IEEE icmla)

作者： Poss, Christian Ibragimov, Olimjon Indreswaran, Anoshan Gutsche, Nils Irrenhauser, thomas Prueglmeier, Marco Goehring, Daniel BMW Grp Logist Robot Munich Germany BMW Grp Innovat & Ind 4 0 Munich Germany Free Univ Berlin Dahlem Ctr Machine Learning & Robot Berlin Germany

ISBN: (纸本)9781538668054

Due to dynamics, flexibility and diversity in logistics, perception-controlled, intelligent robots are required to automate logistical handling steps. Due to the additional optical influences of the industrial environment, such as labeling or damage, these applications seem predestined for the use of generalizing deep neural networks (DNN). these showed continuous improvements over the last few years based on publicly available data sets. If these DNNs are re-trained based on training data from the industrial environment, a lower performance can be observed. the additional extension of the experiments to international locations of the vehicle plants also showed that a drop in performance can be observed in the implementation of a network trained in Germany, for example, when it is used in America. However, in order to be able to use such robots in the logistic processes in the future, further measures such as a revised composition of training data or their extension by data augmentation are proposed.

关键词： Object Detection Deep Neural Networks Industrial Environment Logistics Robotics

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Sampling Adaptively using the Massart Inequality for Scalable learning

Sampling Adaptively using the Massart Inequality for Scalabl...

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12th international conference on machine learning and applications (icmla)

作者： Chen, Jianhua Xu, Jian Louisiana State Univ Sch Elect Engn & Comp Sci Div Comp Sci & Engn Baton Rouge LA 70803 USA

ISBN: (纸本)9780769551449

With the advent of the "big data" era, the data mining community is facing an increasingly critical problem of developing scalable algorithms capable of mining knowledge from massive amount of data. this paper develops a sampling-based method to address the issue of scalability. We show how to utilize the new, adaptive sampling method in [4] to develop a scalable learning algorithm by boosting, an ensemble learning method. We present experimental results using benchmark data sets from the UC-Irvine ML data repository that confirm the much improved efficiency and thus scalability, and competitive prediction accuracy of the new adaptive boosting method, in comparison with existing approaches.

关键词： Scalable learning Ensemble learning Adaptive Sampling Sample Size Boosting

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Cognitive-Assisted Interactive Labeling of Skin Lesions and Blood Cells 17

Cognitive-Assisted Interactive Labeling of Skin Lesions and ...

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17th IEEE international conference on machine learning and applications (IEEE icmla)

作者： Luus, Francois Akhalwaya, Ismail Khan, Naweed IBM Res AI Johannesburg South Africa IBM Res Africa Johannesburg South Africa Univ Witwatersrand Sch Comp Sci & Appl Math Johannesburg South Africa

ISBN: (纸本)9781538668054

Supervised deep learning depends on labeled datasets to define objective categorization of subject matter, but annotation is typically quite expensive for specialized domains. the ISIC 2017 skin lesion and BCCD blood cell image datasets are used to represent complex medical annotation scenarios, where domain knowledge is not permitted in either preprocessing or feature extraction. A low complexity supervision method is proposed, based on an iterative machine learning algorithm that fulfills the requirements for cognitive-assisted labeling. the visualization and editing of feature spaces is demanded where new label information must be integrated to improve the embedding quality as feedback mechanism. the annotators ability for fast local homogeneity assessment is leveraged through compound labeling prospects, which is the basis for achieving efficient labeling. Improved unsupervised feature extraction is hypothesized to reduce the labeling burden so the best feature extractors are empirically located at the various depths in ImageNet-pretrained convolutional neural networks, including VGG-16, Inception-v4 and Inception-Resnet-v2. Annotator emulation is performed to simulate upper bounds of achievable labeling efficiency and to explore active learning dynamics. A two-fold increase in efficiency is shown in the case of partial labeling, despite the complexity of the skin lesion data and the marginal improvement with pretrained features.

关键词： feature extraction active learning semisupervised learning dimensionality reduction clustering

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