[PDF] Top 20 Improving Performance of Classifiers using Rotational Feature Selection Scheme
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Improving Performance of Classifiers using Rotational Feature Selection Scheme
... the performance of existing ...by improving the performance of well-known supervised classifiers like Naive Bayesian, Decision Tree and k-Nearest ...developed rotational ... See full document
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Feature Extraction based Approaches for Improving the Performance of Intrusion Detection Systems
... variety of algorithms and intrusion detection system model, dimension reduction methods are often used to select important features and to reduce dimension size for saving computational ...groups of ... See full document
6
A Parallel Computing Hybrid Approach for Feature Selection
... number of features, which in most cases means poor performance from the learning ...curse of dimensionality ...risk of overfitting, which means adapting a learning algorithm so much to the ... See full document
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AN ANN APPROACH FOR NETWORK INTRUSION DETECTION USING ENTROPY BASED FEATURE SELECTION
... number of malicious users are also growing day-by-day posing a serious problem in distinguishing between normal and abnormal behavior of users in the ...area of intrusion detection which essentially ... See full document
15
Classifier ensemble feature selection for automatic fault diagnosis
... regions of an image quickly while spending more computation on promising object-like ...weak classifiers, the strong algorithms have to cope with a reduced set of regions and are consequently faster ... See full document
112
Analysis of feature selection on the performance of multimodal keystroke dynamics biometric systems
... four of the general objectives set in the introductory chapter of this report were reached: the analysis of the impact of feature selection on classification accuracy has show ... See full document
54
Selecting Features of Single Lead ECG Signal for Automatic Sleep Stages Classification using Correlation-based Feature Subset Selection
... Correlation-based Feature Subset Selection (CFS) is used to choose the features which are significant to determine the sleep ...evaluated using four different characteristic classifiers ... See full document
10
Feature selection strategies for improving data-driven decision support in bank telemarketing
... type of phone ...subscription performance. In fact, other reason may favor the split using the offered rate: the expert mentioned that eventually a DM model would optimally use different features ... See full document
113
Feature selection for chemical sensor arrays using mutual information.
... comparison of classification results among a number of the different classifiers – support vector machine (SVM), k-nearest neighbours (kNN), Bayesian networks (BN), neural networks (NN), mutual ... See full document
17
Improving Variable Selection and Mammography-based Machine Learning Classifiers for Breast Cancer CADx
... set of 16 image-based (intensity and shape) descriptors extracted from MCs lesions on ipsilateral images view (MLO and ...classification performance of the kNN classifier was 0.83 for a dataset ... See full document
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ANALYSE THE PERFORMANCE OF ENSEMBLE CLASSIFIERS USING SAMPLING TECHNIQUES
... records of original dataset at random but with ...subset of feature from the original features that are used to split the node in growing each tree in the ...value of subset of ... See full document
4
Discriminating between lysine sumoylation and lysine acetylation using mRMR feature selection and analysis.
... Features of PSSM conservation ...status of a residue in a peptide was measured using Position Specific Iterative BLAST (PSI-BLAST) [35], which is a powerful sequence searching ...probability ... See full document
10
Efficient feature for classification of eye movements using electrooculography signals
... activities of human eye. The understanding, characterization, and classification of eye movements based on EOG signals play an important role as fundamental for a human-computer interface (HCI) ...variety ... See full document
10
Improving the Performance of Text Summarization
... numbers of clusters and K-means algorithm to find the centre of each clusters and then used QSSM to compute the similarity of each documents in initial ranked list and each cluster ... See full document
132
Information Gain Feature Selection for Multi-Label Classification
... body of research in supervised learning deals with the analysis of single-label data, where instances are associated with a single label from a set of class ...process of predicting the class ... See full document
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Improving Capacity of soft Handoff Performance in Wireless Mobile Communication using Macro Diversity
... transfer of call takes place, when the mobile unit moves far away from old base station and approach toward new base ...ratio of the same signal via mobile switching center ...capacity of the CDMA ... See full document
7
Morphological feature selection and neural classification
... volume of data and speed up the ...performed using the “Minimum Auto Threshold ” algorithm ...result of local miscalculation of the threshold limit ...production of image measurements ... See full document
6
Feature Selection via Chaotic Antlion Optimization.
... the feature selection problem that considers a “chaotic” version of the antlion optimizer method, a nature-inspired algorithm that mimics the hunting mechanism of antlions in ...exploration ... See full document
21
Markov Blanket based Feature Selection: A Review of Past Decade
... space of possible combination of features and evaluates each subset by utilizing the learning algorithm of interest as a black box ...each feature subset, and the outcome is tailored to a ... See full document
8
A new method for feature selection based on fuzzy similarity measures using multi objective genetic algorithm
... complexity of many commonly used operations are highly dependent ...poor performance. Feature selection (FS) [1] is a process which attempts to select features which are information-rich ... See full document
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