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A Hyper Meta-Heuristic Cascaded Support Vector Machines for Big Data Cyber-Security

2019 International journal of recent technology and engineering  
Organisation processing of information inward and outward is huge in quantity and determining a threat amidst of information is challengeable.  ...  By overcoming this issue, developed research system designs an Ensemble Support Vector Machine (ESVM) framework for big data cyber security.  ...  Hence there is a need of security monitoring system strongly for huge dataset in networking. A work against malware attacks are proposed in this research.  ... 
doi:10.35940/ijrte.d5330.118419 fatcat:yx4adfy4ondyzdazdsql76msby

A Scalable Heuristic Classifier for Huge Datasets: A Theoretical Approach [chapter]

Hamid Parvin, Behrouz Minaei-Bidgoli, Sajad Parvin
2011 Lecture Notes in Computer Science  
This paper proposes a heuristic classifier ensemble to improve the performance of learning in multiclass problems.  ...  Although the more accurate classifier leads to a better performance, there is another approach to use many inaccurate classifiers while each one is specialized for a few data in the problem space and using  ...  So first an arbitrary number of binary classifier ensembles are added to main classifier. Then results of all these classifier are given to a set of a heuristic based ensemble.  ... 
doi:10.1007/978-3-642-25085-9_45 fatcat:jvlehjxbizfqfmm2qjkdx5w62a

Squeezing the Ensemble Pruning: Faster and More Accurate Categorization for News Portals [chapter]

Cagri Toraman, Fazli Can
2012 Lecture Notes in Computer Science  
Recent studies show that ensemble pruning works as effective as traditional ensemble of classifiers (EoC).  ...  The most crucial two phases of text categorization are training classifiers and assigning labels to new documents; but the latter is more important for efficiency of such applications.  ...  For repeatability of the experiments we use the Reuters-21578, which is a wellknown benchmark dataset [2] .  ... 
doi:10.1007/978-3-642-28997-2_52 fatcat:iicldyrjdza5zh4fvterkgzh6q

DNA Gene Expression Classification with Ensemble Classifiers Optimized by Speciated Genetic Algorithm [chapter]

Kyung-Joong Kim, Sung-Bae Cho
2005 Lecture Notes in Computer Science  
the optimal ensembles for the cancer classification.  ...  As molecular information is increasing for the cancer classification, a lot of techniques have been proposed and utilized to classify and predict the cancers from gene expression profiles.  ...  Therefore, forming an ensemble of all the feature-classifier pairs is not a good heuristic.  ... 
doi:10.1007/11590316_104 fatcat:xk6a423dy5hyngi3qjmax2e24m

A Hybrid Approach for Feature Subset Selection using Ant Colony Optimization and Multi-Classifier Ensemble

Anam Naseer, Waseem Shahzad, Arslan Ellahi
2018 International Journal of Advanced Computer Science and Applications  
It is a filter based method in which a classifier ensemble is coupled with Ant colony optimization algorithm to enhance the predictive accuracy of filters.  ...  In this paper, we proposed a hybrid approach for feature subset selection.  ...  A multi-classifier ensemble is used iteratively for selecting the best subset of different convergence threshold value and also for final subset selection in a novel way.  ... 
doi:10.14569/ijacsa.2018.090142 fatcat:armvbgepuzhpznhzee2r5oufxu

Research on Feature Selection using SVM

2019 International journal of recent technology and engineering  
A very fast and efficient classification algorithm is imperative to any application. Nowadays all kinds of applications produce a huge volume of data.  ...  To fill this gap, feature selection is integrated with classifiers, as Feature selection has proved its impact on performance of classifiers. SVM is one of the most frequently used classifier.  ...  They In [16] , a novel classifier ensemble was proposed by using ensemble detection models. Data level and feature level detection models are generated.  ... 
doi:10.35940/ijrte.d5279.118419 fatcat:gzb3m7qvszapxaixbk7ronxgke

Multi-Objective Heuristic Feature Selection for Speech-Based Multilingual Emotion Recognition

Christina Brester, Eugene Semenkin, Maxim Sidorov
2016 Journal of Artificial Intelligence and Soft Computing Research  
To diminish the drawbacks of genetic algorithms, which are applied as optimizers, we design a parallel multicriteria heuristic procedure based on an island model.  ...  According to the results obtained, a high level of emotion recognition was achieved (up to a 12.97% relative improvement compared with the best F-score value on the full set of attributes).  ...  This heuristic feature selection approach was investigated in combination with a number of classifiers (MLP, SMO, LOGIT) and with the ensemble of these models.  ... 
doi:10.1515/jaiscr-2016-0018 fatcat:ma3v4z6k4fb4fdwaitffvjvfvm

Bisociative Literature Mining by Ensemble Heuristics [chapter]

Matjaž Juršič, Bojan Cestnik, Tanja Urbančič, Nada Lavrač
2012 Lecture Notes in Computer Science  
The methodology is based on an ensemble of specially tailored text mining heuristics which assign the candidate bridging concepts a bisociation score.  ...  This chapter introduces the system CrossBee (on-line Cross-Context Bisociation Explorer) which implements a methodology that supports the search for hidden links connecting two different domains.  ...  The training dataset is the dataset we employed when developing the methodology, i.e., for creating a set of base heuristics in [7] , as well as for creating the ensemble heuristic presented in this work  ... 
doi:10.1007/978-3-642-31830-6_24 fatcat:gjtizbqpovg2vgytoaly526r6q

A Review on Classification of Data Imbalance using BigData

Ramasubramanian, Hariharan Shanmugasundaram
2021 International Journal of Managing Information Technology  
In the imbalanced dataset, majority classes dominate over minority classes causing the machine learning classifiers to be more biased towards majority classes and also most classification algorithm predicts  ...  Classification using supervised learning method aims to identify the category of the class to which a new data will fall under.  ...  ACKNOWLEDGEMENTS The authors would like to extend sincere thanks to the management for providing us support and environment for carrying out the research and to other fellow colleagues for their support  ... 
doi:10.5121/ijmit.2021.13302 fatcat:7v52ofngqvgyjarvlsrqqzoimm

IALE: Imitating Active Learner Ensembles [article]

Christoffer Löffler, Christopher Mutschler
2020 arXiv   pre-print
We use DAGGER to train the policy on a dataset and later apply it to datasets from similar domains.  ...  However, the performance of AL heuristics depends on the structure of the underlying classifier model and the data.  ...  Since these states are the same for CNN and Resnet-18 classifiers (independant of the datasets) we can transfer a policy trained using a Resnet-18 classifier to a CNN classifier and vice versa.  ... 
arXiv:2007.04637v3 fatcat:wjc7afzbknfwha563a5csvav3e

Optimal Feature Selection using Particle Swarm Optimization with Random Forest Classifier for Lymph Diseases Prediction

2019 International journal of recent technology and engineering  
For FS, genetic algorithm and particle swarm optimization (PSO) algorithm is used whereas random forest (RF) classifier is used for classification lymph diseases dataset.  ...  It is also studied that the FS process improvises the classifier results in a significant manner interms of diverse performance measures.  ...  A disadvantage linked with tree classifiers is its huge variance. It is not unusual for a tiny modification in the training dataset to outcome in a variety of tree in practice.  ... 
doi:10.35940/ijrte.d6791.118419 fatcat:cfqbdugguvaatcd7stpflyrjpi

INGEOTEC at SemEval 2017 Task 4: A B4MSA Ensemble based on Genetic Programming for Twitter Sentiment Analysis

Sabino Miranda-Jiménez, Mario Graff, Eric Sadit Tellez, Daniela Moctezuma
2017 Proceedings of the 11th International Workshop on Semantic Evaluation (SemEval-2017)  
This paper describes the system used in SemEval-2017 Task 4 (Subtask A): Message Polarity Classification for both English and Arabic languages.  ...  Our proposed system is an ensemble of two layers, the first one uses our generic framework for multilingual polarity classification (B4MSA) and the second layer combines all the decision function values  ...  In case of DS dataset, we obtained 11 million tweets for English after processing a huge amount of tweets, and 16 thousand tweets for Arabic (see section 3.2).  ... 
doi:10.18653/v1/s17-2130 dblp:conf/semeval/Miranda-Jimenez17 fatcat:xskwes2unjc43heshy7rtbkksm

ALEAN STACKED ENSEMBLE MODEL(LSEM) TO ENHANCE THE EFFECTIVENESS OF CLASSIFYING DATA WITH HUGE IMBALANCE

N. Elavarasan
2017 International Journal of Advanced Research in Computer Science  
This paper proposes a Lean SVM based Ensemble Model (LSEM) that enables effective classification of data without the need for pre-processing.  ...  A heterogeneous ensemble is created using Random Forest and One-Class SVM. The requirement of partial training data for SVM makes the model lean, enabling faster training.  ...  The proposed Lean Ensemble (LSEM) creates a heterogeneous ensemble model with a heuristic combiner to operate on imbalanced data.  ... 
doi:10.26483/ijarcs.v8i9.4913 fatcat:fd6qypmzj5e2zgitkcbowlg6fy

New Feature Selection Model Based Ensemble Rule Classifiers Method for Dataset Classification

Mohammad Aizat bin Basir, Faudziah binti Ahmad
2017 International Journal of Artificial Intelligence & Applications  
Research process involves 2 phases; finding the optimal set of attributes and ensemble classifiers method for classification task.  ...  The experimental results conducted on public real dataset demonstrate that the ensemble rule classifiers methods consistently show improve classification accuracy on the selected dataset.  ...  Huge accuracy improvement using OneR rule classifier with Boosting method for Spam-base dataset which is more than 10% accuracy increased.  ... 
doi:10.5121/ijaia.2017.8204 fatcat:sbt7hp6lnvczzekvmjxodlwahi

Effective Parameter Optimization & Classification using Bat-Inspired Algorithm with Improving NSSA

2019 International Journal of Engineering and Advanced Technology  
KDD cup 1999 dataset is accessed to develop this predictive model. Bat optimization algorithm identifies the optimal parameter subset.  ...  In this paper, a machine learning-based approach is proposed to identify the pattern of different categories of attacks made in the past.  ...  In fig 4, the accuracy calculated for all the three heuristic algorithms with ML classifiers is projected.  ... 
doi:10.35940/ijeat.a1498.109119 fatcat:jjla5a2lsjef7f5zkwj7pwtlou
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