Search Results - (( feature classification modeling algorithm ) OR ( java implementation mining algorithm ))
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1
Features selection for intrusion detection system using hybridize PSO-SVM
Published 2016“…Hybridize Particle Swarm Optimization (PSO) as a searching algorithm and support vector machine (SVM) as a classifier had been implemented to cope with this problem. …”
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Thesis -
2
Direct approach for mining association rules from structured XML data
Published 2012“…The thesis also provides a two different implementation of the modified FLEX algorithm using a java based parsers and XQuery implementation. …”
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3
A novel hybrid classification model of genetic algorithms, modified k-Nearest Neighbor and developed backpropagation neural network
Published 2014“…In the present study, the results of the implementation of a novel hybrid feature selection-classification model using the above mentioned methods are presented. …”
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Article -
4
Study and Implementation of Data Mining in Urban Gardening
Published 2019“…Using the J48 tree algorithm implemented through WEKA API on a Java Servlet, data provided is processed to derive a health index of the plant, with the possible outcomes set to “Good,” “Okay”, or “Bad”. …”
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5
Selection and optimization of peak features for event-related eeg signals classification / Asrul bin Adam
Published 2017“…The four peak models are named as Dumpala, Acir, Liu, and Dingle models whereas the full features set model consists of 16 peak features. …”
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6
Ideal combination feature selection model for classification problem based on bio-inspired approach
Published 2020“…The important step is to idealize the combined feature selection models by finding the best combination of search method and feature selection algorithms. …”
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Book Section -
7
Formulating new enhanced pattern classification algorithms based on ACO-SVM
Published 2013“…ACO originally deals with discrete optimization problem.In applying ACO for solving SVM model selection problem which are continuous variables, there is a need to discretize the continuously value into discrete values.This discretization process would result in loss of some information and hence affects the classification accuracy and seeking time.In this algorithm we propose to solve SVM model selection problem using IACOR without the need to discretize continuous value for SVM.The second algorithm aims to simultaneously solve SVM model selection problem and selects a small number of features.SVM model selection and selection of suitable and small number of feature subsets must occur simultaneously because error produced from the feature subset selection phase will affect the values of SVM model selection and result in low classification accuracy.In this second algorithm we propose the use of IACOMV to simultaneously solve SVM model selection problem and features subset selection.Ten benchmark datasets were used to evaluate the proposed algorithms.Results showed that the proposed algorithms can enhance the classification accuracy with small size of features subset.…”
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8
Feature Selection with Harmony Search for Classification: A Review
Published 2021“…This paper gives a general review of feature selection with Harmony Search (HS) algorithm for classification in various application. …”
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Proceeding -
9
Scalable approach for mining association rules from structured XML data
Published 2009“…Many techniques have been proposed to tackle the problem of mining XML data we study the various techniques to mine XML data and yet We presented a java based implementation of FLEX algorithm for mining XML data.…”
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10
Mining association rules from structured XML data
Published 2009“…Many techniques have been proposed to tackle the problem of mining XML data. We study the various techniques to mine XML data and yet We presented a java based implementation of FLEX algorithm for mining XML data.…”
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11
An accurate infant cry classification system based on continuos hidden Markov model
Published 2023Subjects:Conference Paper -
12
A web-based implementation of k-means algorithms
Published 2022“…This stinginess of proximity measures in data mining tools is stifling the performance of the algorithm. …”
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Final Year Project / Dissertation / Thesis -
13
Feature selection and model selection algorithm using incremental mixed variable ant colony optimization for support vector machine classifier
Published 2013“…Support Vector Machine (SVM) is a present day classification approach originated from statistical approaches.Two main problems that influence the performance of SVM are selecting feature subset and SVM model selection. …”
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14
Optimization of attribute selection model using bio-inspired algorithms
Published 2019“…Such a finding indicates that bio-inspired algorithms can contribute in identifying the few most important features to be used in data mining model construction.…”
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15
Classification of hand gestures from EMG signals / Diaa Albitar
Published 2022“…The outcome shows that classification model using K-NN algorithm with 14 features has the highest classification accuracy, sensitivity and predictivity of97.99%, 94.77% and 92.95% respectively compared to other models from SVM and CNN. …”
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16
Behavioural features for mushroom classification
Published 2018“…The Principal Component Analysis (PCA) algorithm is used for selecting the best features for the classification experiment using Decision Tree (DT) algorithm. …”
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17
A hybrid deep CNN model for fast class-incremental food classification / Aymen Taher Ahmed al-Ashwal
Published 2019“…Features are then enhanced by using Tree-based feature selection to reduce the size of each feature and, therefore, enhance classification performance. …”
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18
Extremal region detection and selection with fuzzy encoding for food recognition
Published 2019“…Three algorithms were used to accomplish the task of feature representation. …”
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19
PV fault classification: Impact on accuracy performance using feature extraction in random-forest cross validation algorithm
Published 2024“…This paper introduces a Solar PV Smart Fault Diagnosis and Classification (SFDC) model that harnesses the Random Forest (RF) algorithm in conjunction with Cross-Validation (CV) and an optimized feature extraction (FE) set. …”
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20
Feature Selection using Binary Simulated Kalman Filter for Peak Classification of EEG Signals
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Conference or Workshop Item
