Search Results - (( data optimization method algorithm ) OR ( based application mining algorithm ))
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1
Logistic regression methods for classification of imbalanced data sets
Published 2012“…Hence, it is required to develop effective imbalanced LR-based methods to be widely used in data mining applications. …”
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Thesis -
2
Enhanced AI-based anomaly detection method in the intrusion detection system (IDS) / Kayvan Atefi
Published 2019“…Experiments demonstrate and prove that the proposed EBPSO method produces better accuracy mining data and selecting subset of relevant features comparing other algorithms. …”
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3
Optimizing OLAP heterogeneous computing based on Rabin-Karp algorithm
Published 2013“…OLAP has been considered as a powerful method for analysing excessive size of data that works closely to intelligent business, medical fields. …”
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Proceeding Paper -
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Long-term electrical energy consumption: Formulating and forecasting via optimized gene expression programming / Seyed Hamidreza Aghay Kaboli
Published 2018“…To assess the applicability and accuracy of the proposed method for long-term electrical energy consumption, its estimates are compared with those obtained from artificial neural network (ANN), support vector regression (SVR), adaptive neuro-fuzzy inference system (ANFIS), rule-based data mining algorithm, GEP, linear, quadratic and exponential models optimized by particle swarm optimization (PSO), cuckoo search algorithm (CSA), artificial cooperative search (ACS) algorithm and backtracking search algorithm (BSA). …”
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5
Optimization of attribute selection model using bio-inspired algorithms
Published 2019“…The aim of this paper is to investigate the use of bio-inspired search algorithms in producing optimal attribute set. This is achieved in two stages; 1) create attribute selection models by combining search method and feature selection algorithms, and 2) determine an optimized attribute set by employing bio-inspired algorithms.Classification performance of the produced attribute set is analyzed based on accuracy and number of selected attributes. …”
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Predicting Customer Buying Decisions for Online Shopping with Unbalanced Data Set
Published 2022“…Weka, a data mining tool, provides the facility to classify the data set with different machine learning algorithms. …”
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Final Year Project / Dissertation / Thesis -
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Lexicon-based and immune system based learning methods in Twitter sentiment analysis
Published 2016“…In future work, the accuracy of proposed model can be strengthened by comparative study with other heuristic based searching algorithms such as genetic algorithm, ant colony optimization, swam algorithms and etc.…”
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Conference or Workshop Item -
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Application of Optimization Methods for Solving Clustering and Classification Problems
Published 2011“…In this study we show how to apply a particular class of optimization methods known as pattern search methods to address these challenges. …”
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9
Feature and Instances Selection for Nearest Neighbor Classification via Cooperative PSO
Published 2014“…Therefore, it can be solved by using population-based techniques such as Genetic Algorithm and Particle Swarm Optimization. This paper proposes the integration of feature reduction and data reduction for fuzzy modeling using Cooperative Binary Particle Swarm Optimization (CBPSO). …”
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Conference or Workshop Item -
10
Clustering of rainfall data using k-means algorithm
Published 2019“…Clustering algorithms in data mining is the method for extracting useful information for a given data. …”
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11
Rule pruning techniques in the ant-miner classification algorithm and its variants: A review
Published 2018“…Rule-based classification is considered an important task of data classification.The ant-mining rule-based classification algorithm, inspired from the ant colony optimization algorithm, shows a comparable performance and outperforms in some application domains to the existing methods in the literature.One problem that often arises in any rule-based classification is the overfitting problem. …”
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12
Integrated approach using data mining-based decision tree and object-based image analysis for high-resolution urban mapping of WorldView-2 satellite sensor data
Published 2016“…The developed DT algorithm was applied to object-based classifications in the first study area. …”
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Article -
13
Enhanced dimensionality reduction methods for classifying malaria vector dataset using decision tree
Published 2021“…The classifier uses Decision tree on the reduced mosquito anopheles gambiae dataset to enhance the accuracy and scalability in the gene expression analysis. The proposed algorithm is used to fetch relevant features based from the high-dimensional input feature space. …”
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Hybrid metaheuristics for QOS-aware service composition / Hadi Naghavipour
Published 2022“…An absolute majority of base algorithms for this problem were nature-inspired and population-based metaheuristics extended to complementary methods in hybrid solutions. …”
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16
Framework for stream clustering of trajectories based on temporal micro clustering technique
Published 2018“…In the online phase, the stream clustering algorithm for trajectories based on the lifespan of the cluster is proposed (CC_TRS) to overcome the limitations of the time window technique. …”
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17
Classification with degree of importance of attributes for stock market data mining
Published 2004“…The SVM is a training algorithm for learning classification and regression rules from data [7]. …”
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The Parallel Fuzzy C-Median Clustering Algorithm Using Spark for the Big Data
Published 2024“…Therefore, we develop a Parallel Fuzzy C-Median Clustering Algorithm Using Spark for Big Data that can handle large datasets while maintaining high accuracy and scalability. …”
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Article -
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Improvement on rooftop classification of worldview-3 imagery using object-based image analysis
Published 2019“…Then, the classifier (support vector machine (SVM) and data mining (DM) algorithm, decision tree (DT) were applied on each fusion image and their accuracy were evaluated. …”
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20
Fireflyclust: an automated hierarchical text clustering approach
Published 2017“…The proposed clustering method operates based on five phases: data pre-processing, clustering, item re-location, cluster selection and cluster refinement. …”
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