MReC4.5: C4.5 Ensemble Classification with MapReduce

2018 ◽

Vol 7 (1) ◽

pp. 57-72

Author(s):

H.P. Vinutha ◽

Poornima Basavaraju

Keyword(s):

Feature Selection ◽

Intrusion Detection ◽

Detection Rate ◽

Information Gain ◽

False Positive Rate ◽

Ensemble Classifier ◽

Ensemble Classification ◽

Chi Square ◽

Traffic Pattern ◽

Data Mining Algorithms

Day by day network security is becoming more challenging task. Intrusion detection systems (IDSs) are one of the methods used to monitor the network activities. Data mining algorithms play a major role in the field of IDS. NSL-KDD'99 dataset is used to study the network traffic pattern which helps us to identify possible attacks takes place on the network. The dataset contains 41 attributes and one class attribute categorized as normal, DoS, Probe, R2L and U2R. In proposed methodology, it is necessary to reduce the false positive rate and improve the detection rate by reducing the dimensionality of the dataset, use of all 41 attributes in detection technology is not good practices. Four different feature selection methods like Chi-Square, SU, Gain Ratio and Information Gain feature are used to evaluate the attributes and unimportant features are removed to reduce the dimension of the data. Ensemble classification techniques like Boosting, Bagging, Stacking and Voting are used to observe the detection rate separately with three base algorithms called Decision stump, J48 and Random forest.

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Fighting Together against the Pandemic: Learning Multiple Models on Tomography Images for COVID-19 Diagnosis

AI ◽

10.3390/ai2020016 ◽

2021 ◽

Vol 2 (2) ◽

pp. 261-273

Author(s):

Mario Manzo ◽

Simone Pellino

Keyword(s):

Network Architecture ◽

State Of The Art ◽

Ensemble Classification ◽

Effective Vaccine ◽

Rt Pcr ◽

Neural Network Architecture ◽

Experimental Phase ◽

Different Types ◽

Polymerase Chain ◽

Better Than

COVID-19 has been a great challenge for humanity since the year 2020. The whole world has made a huge effort to find an effective vaccine in order to save those not yet infected. The alternative solution is early diagnosis, carried out through real-time polymerase chain reaction (RT-PCR) tests or thorax Computer Tomography (CT) scan images. Deep learning algorithms, specifically convolutional neural networks, represent a methodology for image analysis. They optimize the classification design task, which is essential for an automatic approach with different types of images, including medical. In this paper, we adopt a pretrained deep convolutional neural network architecture in order to diagnose COVID-19 disease from CT images. Our idea is inspired by what the whole of humanity is achieving, as the set of multiple contributions is better than any single one for the fight against the pandemic. First, we adapt, and subsequently retrain for our assumption, some neural architectures that have been adopted in other application domains. Secondly, we combine the knowledge extracted from images by the neural architectures in an ensemble classification context. Our experimental phase is performed on a CT image dataset, and the results obtained show the effectiveness of the proposed approach with respect to the state-of-the-art competitors.

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Ensemble classification technique for heart disease prediction with meta-heuristic-enabled training system

Bio-Algorithms and Med-Systems ◽

10.1515/bams-2020-0033 ◽

2020 ◽

Vol 0 (0) ◽

Author(s):

Parvathaneni Rajendra Kumar ◽

Suban Ravichandran ◽

Satyala Narayana

Keyword(s):

Heart Disease ◽

Dimensionality Reduction ◽

Research Work ◽

Feature Reduction ◽

Ensemble Classification ◽

Support Vector ◽

Disease Prediction ◽

Central Tendency ◽

Sea Lion ◽

Qualitative Variation

AbstractObjectivesThis research work exclusively aims to develop a novel heart disease prediction framework including three major phases, namely proposed feature extraction, dimensionality reduction, and proposed ensemble-based classification.MethodsAs the novelty, the training of NN is carried out by a new enhanced optimization algorithm referred to as Sea Lion with Canberra Distance (S-CDF) via tuning the optimal weights. The improved S-CDF algorithm is the extended version of the existing “Sea Lion Optimization (SLnO)”. Initially, the statistical and higher-order statistical features are extracted including central tendency, degree of dispersion, and qualitative variation, respectively. However, in this scenario, the “curse of dimensionality” seems to be the greatest issue, such that there is a necessity of dimensionality reduction in the extracted features. Hence, the principal component analysis (PCA)-based feature reduction approach is deployed here. Finally, the dimensional concentrated features are fed as the input to the proposed ensemble technique with “Support Vector Machine (SVM), Random Forest (RF), K-Nearest Neighbor (KNN)” with optimized Neural Network (NN) as the final classifier.ResultsAn elaborative analyses as well as discussion have been provided by concerning the parameters, like evaluation metrics, year of publication, accuracy, implementation tool, and utilized datasets obtained by various techniques.ConclusionsFrom the experiment outcomes, it is proved that the accuracy of the proposed work with the proposed feature set is 5, 42.85, and 10% superior to the performance with other feature sets like central tendency + dispersion feature, central tendency qualitative variation, and dispersion qualitative variation, respectively.ResultsFinally, the comparative evaluation shows that the presented work is appropriate for heart disease prediction as it has high accuracy than the traditional works.

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Deep-MEG: spatiotemporal CNN features and multiband ensemble classification for predicting the early signs of Alzheimer’s disease with magnetoencephalography

Neural Computing and Applications ◽

10.1007/s00521-021-06105-4 ◽

2021 ◽

Author(s):

Antonio Giovannetti ◽

Gianluca Susi ◽

Paola Casti ◽

Arianna Mencattini ◽

Sandra Pusil ◽

...

Keyword(s):

Alzheimer’S Disease ◽

Alzheimer's Disease ◽

Brain Regions ◽

Ensemble Classification ◽

The Novel ◽

Ensemble Classifiers ◽

Deep Convolutional Neural Networks ◽

Early Signs ◽

Data Representations ◽

The Brain

AbstractIn this paper, we present the novel Deep-MEG approach in which image-based representations of magnetoencephalography (MEG) data are combined with ensemble classifiers based on deep convolutional neural networks. For the scope of predicting the early signs of Alzheimer’s disease (AD), functional connectivity (FC) measures between the brain bio-magnetic signals originated from spatially separated brain regions are used as MEG data representations for the analysis. After stacking the FC indicators relative to different frequency bands into multiple images, a deep transfer learning model is used to extract different sets of deep features and to derive improved classification ensembles. The proposed Deep-MEG architectures were tested on a set of resting-state MEG recordings and their corresponding magnetic resonance imaging scans, from a longitudinal study involving 87 subjects. Accuracy values of 89% and 87% were obtained, respectively, for the early prediction of AD conversion in a sample of 54 mild cognitive impairment subjects and in a sample of 87 subjects, including 33 healthy controls. These results indicate that the proposed Deep-MEG approach is a powerful tool for detecting early alterations in the spectral–temporal connectivity profiles and in their spatial relationships.

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