DOCSLIB.ORG

Random Hinge Forest for Differentiable Learning

Total Page：16

File Type：pdf, Size：1020Kb

Download full-text PDF Read full-text
Random Hinge Forest for Differentiable Learning Random Hinge Forest for Differentiable Learning Nathan Lay Adam P. Harrison Sharon Schreiber Gitesh Dawer 1 Adrian Barbu 1 Abstract ited to fixed features and can plateau on test performance regardless of the training set size. Deep neural networks, We propose random hinge forests, a simple, effi- however, are difficult to develop, train and even interpret. cient, and novel variant of decision forests. Im- As a consequence, many researchers shy away from explor- portantly, random hinge forests can be readily in- ing and developing their own network architectures and of- corporated as a general component within arbi- ten use and tweak pre-trained off-the-shelf models (for ex- trary computation graphs that are optimized end- ample (Simonyan & Zisserman, 2014; Ronneberger et al., to-end with stochastic gradient descent or vari- 2015; He et al., 2016)) for their purposes. ants thereof. We derive random hinge forest and ferns, focusing on their sparse and efficient na- We propose a novel formulation of random forest, the ran- ture, their min-max margin property, strategies dom hinge forest, that addresses its predecessor’s greedy to initialize them for arbitrary network architec- learning limitations. Random hinge forest is a differen- tures, and the class of optimizers most suitable tiable learning machine for use in arbitrary computation for optimizing random hinge forest. The perfor- graphs. This enables it to learn in an end-to-end fashion, mance and versatility of random hinge forests are benefit from learnable feature representations, as well as demonstrated by experiments incorporating a va- operate in concert with other computation graph mecha- riety of of small and large UCI machine learn- nisms. Random hinge forest also addresses some limita- ing data sets and also ones involving the MNIST, tions present in other formulations of differentiable deci- Letter, and USPS image datasets. We compare sion trees and forests, namely efficiency and numerical sta- random hinge forests with random forests and bility. We show that random hinge forest evaluation and the more recent backpropagating neural decision gradient evaluation are logarithmic in evaluation complex- forests. ity, compared to exponential complexity like previously de- scribed methods, and that random hinge forest is robust to activation/decision function saturation and loss of pre- 1. Introduction cision. Lastly, we show that random hinge forest is better than random forest and comparable to the state-of-the-art Random forest (Breiman, 2001) is a popular and widely decision forest, neural decision forest (Kontschieder et al., used ensemble learning algorithm. With constituent mod- 2015). els being decision trees, its formulation offers some level This paper first describes a series of related works and of interpretability and intuition. It also tends to generalize how they are different, then formulates the random hinge well with modest parameter tuning for a variety learning tree, ferns and forest. Then a series of experiments and tasks and data sets. However, the recent success of deep ar- results are presented on UCI data sets and MNIST. We tificial neural networks have revealed limitations of random arXiv:1802.03882v2 [stat.ML] 1 Mar 2018 compare the performance of the proposed methods with forest and similar greedy learning algorithms. Deep arti- random forest (Breiman, 2001) and neural decision for- ficial neural networks have exhibited state-of-the-art and est (Kontschieder et al., 2015), discuss the findings and fi- even super human performance on some types of tasks. nally conclude the paper. The success of deep neural networks can at least be partly attributed to both its ability to learn parameters in an end- to-end fashion with backpropagation and its ability to scale 2. Related Work on very large data sets. By contrast, random forest is lim- The forerunner of this work is random forest which 1Department of Statistics, Florida State University, Tallahas- was first described by (Amit & Geman, 1997) and later see, FL. Correspondence to: Nathan Lay <[email protected]>. by (Breiman, 2001). Random forest is an ensemble of ran- th dom decision trees that are generally aggregated by voting Proceedings of the 35 International Conference on Machine or averaging. Each random tree is trained on a bootstrap- Learning, Stockholm, Sweden, PMLR 80, 2018. Copyright 2018 by the author(s). aggregated training sample in a greedy and randomized Random Hinge Forest for Differentiable Learning divide-and-conquer fashion. Random trees learn by opti- done for training efficiency and to keep non-linearity. It mizing a task-specific gain function as it divides and par- also, to some extent, helps prevent saturation problems that titions the training data. Gain functions have been defined lead to near-zero gradients with sigmoid activations. To for both classification and regression as well as a plethora the best knowledge of the author, differentiable decision of specialized tasks such as object localization (Gall & trees have always relied on sigmoid-like decision functions Lempitsky, 2013; Criminisi et al., 2010). Trees are also that resemble the activation functions of artificial neural found as components used in other learning algorithms networks. This reliance of sigmoid decision functions in such as boosting (Friedman, 2001). fuzzy decision trees, the plethora of numerical problems with sigmoid activation functions, and the very successful The works of (Suarez´ & Lutsko, 1999; Kontschieder et al., use of ReLU in (Krizhevsky et al., 2012) provided clues to 2015) make partitioning thresholds fuzzy by representing formulate random hinge forest. the threshold operation as a differentiable sigmoid. The objective is to make a decision tree that is differentiable Random hinge forest is different from random forest in that for end-to-end optimization. When traversing these fuzzy constituent trees are all inferred simultaneously in an end- trees, each decision will result in a degree of membership to-end fashion instead of with greedy optimization per tree. in the left and right partitioned sets. For example, if the However, the trees of random forest can currently learn de- decision produces a value of 0:4, then the decision results cision structures not easily described by random hinge trees in 0:4 membership in one partition and 0:6 membership in as formulated here. And where random trees deliberately the other. When the decision process is taken as a whole, choose splitting features that optimize information gain, each resulting partition will have a membership that is a random hinge trees indirectly adjust learnable splitting fea- generally defined as a path-specific product of these deci- tures to fit their randomized decision structure. Random sions producing, however small, non-zero membership for hinge forest is also different from the works of (Suarez´ & all partitions. The work of (Kontschieder et al., 2015) also Lutsko, 1999; Kontschieder et al., 2015) and fuzzy deci- introduces an alternating step for training the predictions sion trees in general in that random hinge trees use Re- and decisions and goes one step further and describes si- LUs for decision functions instead of sigmoids. This re- multaneously optimizing all trees of a forest in an end-to- tains the desirable evaluation behavior of decision trees. end fashion. The work (Schulter et al., 2013) globally opti- Where fuzzy decision trees admit a degree of membership mizes a forest against an arbitrary loss function in a gradi- to several partitions of the feature space, random hinge ent boost framework by gradually building the constituent trees admit membership to only one partition as with crisp trees up from root to leaf in a breadth-first order. decision trees. This also implies that for a depth D tree, random hinge trees need only evaluate D decisions, while The work of (Ozuysal et al., 2010) develops a constrained fuzzy trees need to evaluate on the order of 2D decisions. version of a decision tree known as a random fern. A sin- Like (Kontschieder et al., 2015), random hinge forest can gle random fern behaves like a checklist of tests on features simultaneously learn all trees in an end-to-end fashion. that each produce a yes/no answer. Then the combination However, random hinge forest is not limited to probabil- of yes/no answers is used to look up a prediction. Unlike ity prediction or leaf purity loss and does not need alter- random forest that are aggregated by averaging or voting, nating learning steps for leaf weights and thresholds and random ferns are aggregated via a semi-na¨ıve Bayes for- can be trained in the usual forward-backward pass done in mulation. And unlike greedy learning decision trees, each computation graphs. The work of (Schulter et al., 2013) random fern is trained by picking a random subset of bi- also builds up trees in an iterative fashion while random nary features and then binning the training examples in the hinge forest assume complete trees and a randomized ini- leaves. These leaves are then normalized to be used in a tial state. Both random hinge forest and alternating deci- semi-na¨ıve Bayes aggregation. sion forest aim to optimize global loss functions, but the Random hinge forest also resembles the models learned former can operate as part of a general computation graph. by multivariate adaptive regression splines (MARS) (Fried- This work also presents the random hinge fern which bares man, 1991). MARS is a learning method that fits a linear the same decision constraint as random fern, but we have combination of hinge functions in a greedy fashion. This is not explored semi-na¨ıve Bayes aggregation. This work also done in two passes where hinge functions are progressively provides a way to train the random hinge fern in an end-to- added to fit the training data in the first pass, and then some end fashion which, to the best of our knowledge, has never hinge functions are removed in the second pass to help pro- been done for random ferns.
Load more

Recommended publications
  • Malware Classification with BERT
    San Jose State University SJSU ScholarWorks Master's Projects Master's Theses and Graduate Research Spring 5-25-2021 Malware Classification with BERT Joel Lawrence Alvares Follow this and additional works at: https://scholarworks.sjsu.edu/etd_projects Part of the Artificial Intelligence and Robotics Commons, and the Information Security Commons Malware Classification with Word Embeddings Generated by BERT and Word2Vec Malware Classification with BERT Presented to Department of Computer Science San José State University In Partial Fulfillment of the Requirements for the Degree By Joel Alvares May 2021 Malware Classification with Word Embeddings Generated by BERT and Word2Vec The Designated Project Committee Approves the Project Titled Malware Classification with BERT by Joel Lawrence Alvares APPROVED FOR THE DEPARTMENT OF COMPUTER SCIENCE San Jose State University May 2021 Prof. Fabio Di Troia Department of Computer Science Prof. William Andreopoulos Department of Computer Science Prof. Katerina Potika Department of Computer Science 1 Malware Classification with Word Embeddings Generated by BERT and Word2Vec ABSTRACT Malware Classification is used to distinguish unique types of malware from each other. This project aims to carry out malware classification using word embeddings which are used in Natural Language Processing (NLP) to identify and evaluate the relationship between words of a sentence. Word embeddings generated by BERT and Word2Vec for malware samples to carry out multi-class classification. BERT is a transformer based pre- trained natural language processing (NLP) model which can be used for a wide range of tasks such as question answering, paraphrase generation and next sentence prediction. However, the attention mechanism of a pre-trained BERT model can also be used in malware classification by capturing information about relation between each opcode and every other opcode belonging to a malware family.
    [Show full text]
  • Performance Comparison of Support Vector Machine, Random Forest, and Extreme Learning Machine for Intrusion Detection
    Technological University Dublin ARROW@TU Dublin Articles School of Science and Computing 2018-7 Performance Comparison of Support Vector Machine, Random Forest, and Extreme Learning Machine for Intrusion Detection Iftikhar Ahmad King Abdulaziz University, Saudi Arabia, [email protected] MUHAMMAD JAVED IQBAL UET Taxila MOHAMMAD BASHERI King Abdulaziz University, Saudi Arabia See next page for additional authors Follow this and additional works at: https://arrow.tudublin.ie/ittsciart Part of the Computer Sciences Commons Recommended Citation Ahmad, I. et al. (2018) Performance Comparison of Support Vector Machine, Random Forest, and Extreme Learning Machine for Intrusion Detection, IEEE Access, vol. 6, pp. 33789-33795, 2018. DOI :10.1109/ACCESS.2018.2841987 This Article is brought to you for free and open access by the School of Science and Computing at ARROW@TU Dublin. It has been accepted for inclusion in Articles by an authorized administrator of ARROW@TU Dublin. For more information, please contact [email protected], [email protected]. This work is licensed under a Creative Commons Attribution-Noncommercial-Share Alike 4.0 License Authors Iftikhar Ahmad, MUHAMMAD JAVED IQBAL, MOHAMMAD BASHERI, and Aneel Rahim This article is available at ARROW@TU Dublin: https://arrow.tudublin.ie/ittsciart/44 SPECIAL SECTION ON SURVIVABILITY STRATEGIES FOR EMERGING WIRELESS NETWORKS Received April 15, 2018, accepted May 18, 2018, date of publication May 30, 2018, date of current version July 6, 2018. Digital Object Identifier 10.1109/ACCESS.2018.2841987
    [Show full text]
  • Machine Learning Methods for Classification of the Green
    International Journal of Geo-Information Article Machine Learning Methods for Classification of the Green Infrastructure in City Areas Nikola Kranjˇci´c 1,* , Damir Medak 2, Robert Župan 2 and Milan Rezo 1 1 Faculty of Geotechnical Engineering, University of Zagreb, Hallerova aleja 7, 42000 Varaždin, Croatia; [email protected] 2 Faculty of Geodesy, University of Zagreb, Kaˇci´ceva26, 10000 Zagreb, Croatia; [email protected] (D.M.); [email protected] (R.Ž.) * Correspondence: [email protected]; Tel.: +385-95-505-8336 Received: 23 August 2019; Accepted: 21 October 2019; Published: 22 October 2019 Abstract: Rapid urbanization in cities can result in a decrease in green urban areas. Reductions in green urban infrastructure pose a threat to the sustainability of cities. Up-to-date maps are important for the effective planning of urban development and the maintenance of green urban infrastructure. There are many possible ways to map vegetation; however, the most effective way is to apply machine learning methods to satellite imagery. In this study, we analyze four machine learning methods (support vector machine, random forest, artificial neural network, and the naïve Bayes classifier) for mapping green urban areas using satellite imagery from the Sentinel-2 multispectral instrument. The methods are tested on two cities in Croatia (Varaždin and Osijek). Support vector machines outperform random forest, artificial neural networks, and the naïve Bayes classifier in terms of classification accuracy (a Kappa value of 0.87 for Varaždin and 0.89 for Osijek) and performance time. Keywords: green urban infrastructure; support vector machines; artificial neural networks; naïve Bayes classifier; random forest; Sentinel 2-MSI 1.
    [Show full text]
  • Random Forest Regression of Markov Chains for Accessible Music Generation
    Random Forest Regression of Markov Chains for Accessible Music Generation Vivian Chen Jackson DeVico Arianna Reischer [email protected] [email protected] [email protected] Leo Stepanewk Ananya Vasireddy Nicholas Zhang [email protected] [email protected] [email protected] Sabar Dasgupta* [email protected] New Jersey’s Governor’s School of Engineering and Technology July 24, 2020 *Corresponding Author Abstract—With the advent of machine learning, new generative algorithms have expanded the ability of computers to compose creative and meaningful music. These advances allow for a greater balance between human input and autonomy when creating original compositions. This project proposes a method of melody generation using random forest regression, which in- creases the accessibility of generative music models by addressing the downsides of previous approaches. The solution generalizes the concept of Markov chains while avoiding the excessive computational costs and dataset requirements associated with past models. To improve the musical quality of the outputs, the model utilizes post-processing based on various scoring metrics. A user interface combines these modules into an application that achieves the ultimate goal of creating an accessible generative music model. Fig. 1. A screenshot of the user interface developed for this project. I. INTRODUCTION One of the greatest challenges in making generative music is emulating human artistic expression. DeepMind’s generative II. BACKGROUND audio model, WaveNet, attempts this challenge, but requires A. History of Generative Music large datasets and extensive training time to produce qual- ity musical outputs [1]. Similarly, other music generation The term “generative music,” first popularized by English algorithms such as MelodyRNN, while effective, are also musician Brian Eno in the late 20th century, describes the resource intensive and time-consuming.
    [Show full text]
  • Evaluating the Combination of Word Embeddings with Mixture of Experts and Cascading Gcforest in Identifying Sentiment Polarity
    Evaluating the Combination of Word Embeddings with Mixture of Experts and Cascading gcForest In Identifying Sentiment Polarity by Mounika Marreddy, Subba Reddy Oota, Radha Agarwal, Radhika Mamidi in 25TH ACM SIGKDD CONFERENCE ON KNOWLEDGE DISCOVERY AND DATA MINING (SIGKDD-2019) Anchorage, Alaska, USA Report No: IIIT/TR/2019/-1 Centre for Language Technologies Research Centre International Institute of Information Technology Hyderabad - 500 032, INDIA August 2019 Evaluating the Combination of Word Embeddings with Mixture of Experts and Cascading gcForest In Identifying Sentiment Polarity Mounika Marreddy Subba Reddy Oota [email protected] IIIT-Hyderabad IIIT-Hyderabad Hyderabad, India Hyderabad, India [email protected] [email protected] Radha Agarwal Radhika Mamidi IIIT-Hyderabad IIIT-Hyderabad Hyderabad, India Hyderabad, India [email protected] [email protected] ABSTRACT an effective neural networks to generate low dimensional contex- Neural word embeddings have been able to deliver impressive re- tual representations and yields promising results on the sentiment sults in many Natural Language Processing tasks. The quality of analysis [7, 14, 21]. the word embedding determines the performance of a supervised Since the work of [2], NLP community is focusing on improving model. However, choosing the right set of word embeddings for a the feature representation of sentence/document with continuous given dataset is a major challenging task for enhancing the results. development in neural word embedding. Word2Vec embedding In this paper, we have evaluated neural word embeddings with was the first powerful technique to achieve semantic similarity (i) a mixture of classification experts (MoCE) model for sentiment between words but fail to capture the meaning of a word based classification task, (ii) to compare and improve the classification on context [17].
    [Show full text]
  • 10-601 Machine Learning, Project Phase1 Report Random Forest
    10-601 Machine Learning, Project Phase1 Report Group Name: DEADLINE Team Member: Zhitao Pei (zhitaop), Sean Hao (xinhao) Random Forest Environment: Weka 3.6.11 Data: Full dataset Parameters: 200 trees 400 features 1 seed Unlimited max depth of trees Accuracy: The training takes about half an hour and achieve an accuracy of 39.886%. Explanation: The reason we choose it is that random forest learner will usually give good performance compared to other classifiers. Decision tree is one of the best classifiers as the ranking showed in the class. Random forest is an ensemble of decision trees which is able to reduce the variance and give a better and unbiased result compared to other decision tree. The error mostly occurs when the images are hard to tell the difference simply based on the grid. Multilayer Perceptron Environment: Weka 3.6.11 Parameters: Hidden Layer: 3 Learning Rate: 0.3 Momentum: 0.2 Training Time: 500 Validation Threshold: 20 Accuracy: 27.448% Explanation: I chose Neural Network because I consider the features are independent since they are pixels of picture. To get the relationships between those pixels, a good way is weight different features and combine them to get a result. Multilayer perceptron is perfectly match with my imagination. However, training Multilayer perceptrons consumes huge time once there are many nodes in hidden layer. So I indicates that the node in hidden layer only could be 3. It is bad but that's a sort of trade off. In next phase, I will try to construct different Neural Network structure to reduce the training time and improve model accuracy.
    [Show full text]
  • Evaluation of Adaptive Random Forest Algorithm for Classification of Evolving Data Stream
    DEGREE PROJECT IN TECHNOLOGY, FIRST CYCLE, 15 CREDITS STOCKHOLM, SWEDEN 2020 Evaluation of Adaptive random forest algorithm for classification of evolving data stream AYHAM ALKAZAZ MARWA SAADO KHAROUKI KTH ROYAL INSTITUTE OF TECHNOLOGY SCHOOL OF ELECTRICAL ENGINEERING AND COMPUTER SCIENCE Evaluation of Adaptive random forest algorithm for classification of evolving data stream AYHAM ALKAZAZ & MARWA SAADO KHAROUKI Degree Project in Computer Science Date: August 16, 2020 Supervisor: Erik Fransén Examiner: Pawel Herman School of Electrical Engineering and Computer Science Swedish title: Evaluering av Adaptive random forest algoritm för klassificering av utvecklande dataström Abstract In the era of big data, online machine learning algorithms have gained more and more traction from both academia and industry. In multiple scenarios de- cisions and predictions has to be made in near real-time as data is observed from continuously evolving data streams. Offline learning algorithms fall short in different ways when it comes to handling such problems. Apart from the costs and difficulties of storing these data streams in storage clusters and the computational difficulties associated with retraining the models each time new data is observed in order to keep the model up to date, these methods also don't have built-in mechanisms to handle seasonality and non-stationary data streams. In such streams, the data distribution might change over time in what is called concept drift. Adaptive random forests are well studied and effective for online learning and non-stationary data streams. By using bagging and drift detection mechanisms adaptive random forests aim to improve the accuracy and performance of traditional random forests for online learning.
    [Show full text]
  • Evaluation and Comparison of Word Embedding Models, for Efficient Text Classification
    Evaluation and comparison of word embedding models, for efficient text classification Ilias Koutsakis George Tsatsaronis Evangelos Kanoulas University of Amsterdam Elsevier University of Amsterdam Amsterdam, The Netherlands Amsterdam, The Netherlands Amsterdam, The Netherlands [email protected] [email protected] [email protected] Abstract soon. On the contrary, even non-traditional businesses, like 1 Recent research on word embeddings has shown that they banks , start using Natural Language Processing for R&D tend to outperform distributional models, on word similarity and HR purposes. It is no wonder then, that industries try to and analogy detection tasks. However, there is not enough take advantage of new methodologies, to create state of the information on whether or not such embeddings can im- art products, in order to cover their needs and stay ahead of prove text classification of longer pieces of text, e.g. articles. the curve. More specifically, it is not clear yet whether or not theusage This is how the concept of word embeddings became pop- of word embeddings has significant effect on various text ular again in the last few years, especially after the work of classifiers, and what is the performance of word embeddings Mikolov et al [14], that showed that shallow neural networks after being trained in different amounts of dimensions (not can provide word vectors with some amazing geometrical only the standard size = 300). properties. The central idea is that words can be mapped to In this research, we determine that the use of word em- fixed-size vectors of real numbers. Those vectors, should be beddings can create feature vectors that not only provide able to hold semantic context, and this is why they were a formidable baseline, but also outperform traditional, very successful in sentiment analysis, word disambiguation count-based methods (bag of words, tf-idf) for the same and syntactic parsing tasks.
    [Show full text]
  • Random Forests, Decision Trees, and Categorical Predictors: the “Absent Levels” Problem
    Journal of Machine Learning Research 19 (2018) 1-30 Submitted 9/16; Revised 7/18; Published 9/18 Random Forests, Decision Trees, and Categorical Predictors: The \Absent Levels" Problem Timothy C. Au [email protected] Google LLC 1600 Amphitheatre Parkway Mountain View, CA 94043, USA Editor: Sebastian Nowozin Abstract One advantage of decision tree based methods like random forests is their ability to natively handle categorical predictors without having to first transform them (e.g., by using feature engineering techniques). However, in this paper, we show how this capability can lead to an inherent \absent levels" problem for decision tree based methods that has never been thoroughly discussed, and whose consequences have never been carefully explored. This problem occurs whenever there is an indeterminacy over how to handle an observation that has reached a categorical split which was determined when the observation in question's level was absent during training. Although these incidents may appear to be innocuous, by using Leo Breiman and Adele Cutler's random forests FORTRAN code and the randomForest R package (Liaw and Wiener, 2002) as motivating case studies, we examine how overlooking the absent levels problem can systematically bias a model. Furthermore, by using three real data examples, we illustrate how absent levels can dramatically alter a model's performance in practice, and we empirically demonstrate how some simple heuristics can be used to help mitigate the effects of the absent levels problem until a more robust theoretical solution is found. Keywords: absent levels, categorical predictors, decision trees, CART, random forests 1. Introduction Since its introduction in Breiman (2001), random forests have enjoyed much success as one of the most widely used decision tree based methods in machine learning.
    [Show full text]
  • Random Forests
    1 RANDOM FORESTS Leo Breiman Statistics Department University of California Berkeley, CA 94720 January 2001 Abstract Random forests are a combination of tree predictors such that each tree depends on the values of a random vector sampled independently and with the same distribution for all trees in the forest. The generalization error for forests converges a.s. to a limit as the number of trees in the forest becomes large. The generalization error of a forest of tree classifiers depends on the strength of the individual trees in the forest and the correlation between them. Using a random selection of features to split each node yields error rates that compare favorably to Adaboost (Freund and Schapire[1996]), but are more robust with respect to noise. Internal estimates monitor error, strength, and correlation and these are used to show the response to increasing the number of features used in the splitting. Internal estimates are also used to measure variable importance. These ideas are also applicable to regression. 2 1. Random Forests 1.1 Introduction Significant improvements in classification accuracy have resulted from growing an ensemble of trees and letting them vote for the most popular class. In order to grow these ensembles, often random vectors are generated that govern the growth of each tree in the ensemble. An early example is bagging (Breiman [1996]), where to grow each tree a random selection (without replacement) is made from the examples in the training set. Another example is random split selection (Dietterich [1998]) where at each node the split is selected at random from among the K best splits.
    [Show full text]
  • Deep Learning with Long Short-Term Memory Networks for Financial Market Predictions
    A Service of Leibniz-Informationszentrum econstor Wirtschaft Leibniz Information Centre Make Your Publications Visible. zbw for Economics Fischer, Thomas; Krauss, Christopher Working Paper Deep learning with long short-term memory networks for financial market predictions FAU Discussion Papers in Economics, No. 11/2017 Provided in Cooperation with: Friedrich-Alexander University Erlangen-Nuremberg, Institute for Economics Suggested Citation: Fischer, Thomas; Krauss, Christopher (2017) : Deep learning with long short-term memory networks for financial market predictions, FAU Discussion Papers in Economics, No. 11/2017, Friedrich-Alexander-Universität Erlangen-Nürnberg, Institute for Economics, Nürnberg This Version is available at: http://hdl.handle.net/10419/157808 Standard-Nutzungsbedingungen: Terms of use: Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Documents in EconStor may be saved and copied for your Zwecken und zum Privatgebrauch gespeichert und kopiert werden. personal and scholarly purposes. Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle You are not to copy documents for public or commercial Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich purposes, to exhibit the documents publicly, to make them machen, vertreiben oder anderweitig nutzen. publicly available on the internet, or to distribute or otherwise use the documents in public. Sofern die Verfasser die Dokumente unter Open-Content-Lizenzen (insbesondere CC-Lizenzen) zur Verfügung gestellt haben sollten, If the documents
    [Show full text]
  • A Hybrid Random Forest Based Support Vector Machine Classification Supplemented by Boosting by T Arun Rao & T.V
    Global Journal of Computer Science and Technology : C Software & Data Engineering Volume 14 Issue 1 Version 1.0 Year 2014 Type: Double Blind Peer Reviewed International Research Journal Publisher: Global Journals Inc. (USA) Online ISSN: 0975-4172 & Print ISSN: 0975-4350 A Hybrid Random Forest based Support Vector Machine Classification supplemented by boosting By T arun Rao & T.V. Rajinikanth Acharya Nagarjuna University, India Abstract - This paper presents an approach to classify remote sensed data using a hybrid classifier. Random forest, Support Vector machines and boosting methods are used to build the said hybrid classifier. The central idea is to subdivide the input data set into smaller subsets and classify individual subsets. The individual subset classification is done using support vector machines classifier. Boosting is used at each subset to evaluate the learning by using a weight factor for every data item in the data set. The weight factor is updated based on classification accuracy. Later the final outcome for the complete data set is computed by implementing a majority voting mechanism to the individual subset classification outcomes. Keywords: boosting, classification, data mining, random forest, remote sensed data, support vector machine. GJCST-C Classification: G.4 A hybrid Random Forest based Support Vector Machine Classification supplemented by boosting Strictly as per the compliance and regulations of: © 2014. Tarun Rao & T.V. Rajinikanth . This is a research/review paper, distributed under the terms of the Creative Commons Attribution- Noncommercial 3.0 Unported License http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction inany medium, provided the original work is properly cited.
    [Show full text]