An Introduction to the Weka Data Mining System (Abstract Only)

Russell, Ingrid; Markov, Zdravko

doi:10.1145/3017680.3017821

Cited by 27 publications

(13 citation statements)

References 0 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…However, advancement in technology has made available machine learning software repositories which are a collection of various machine learning algorithms, pre-programmed, that enables a complete and effective implementation of learning problems without necessarily involving the technicality of writing any single line of code. These provisions include Waikato Environment for Knowledge Analysis (WEKA) [2,50], Mallet, MatLab, SciKit-Learn, RapidMiner (formerly known as YALE), R-Programming, Orange, KNIME, NLTK etc. However, for the purpose of this study, WEKA Version 3.8.2, which is one of the leading open source and widely used data mining tool [51,52] was chosen and deployed for all the experiments conducted.…”

Section: The Experimentsmentioning

confidence: 99%

“…The adoption of machine learning methods for Android malware detection has removed the daunting task associated with having to continuously update detection engines with new patterns manually [1]. Learning processes involve the analysis of data using designed algorithms to develop models that can be used to find patterns and regularities in any future set of data [2]. The algorithms are made to learn from data that exist to become intelligent enough to predict future events or instances of data.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Anomaly Android Malware Detection: A Comparative Analysis of Six Classifiers

Gyunka¹,

Abikoye

Adekunle³

2021

Communications in Computer and Information Science

View full text Add to dashboard Cite

The high proliferation rate of Android devices has exposed the platform to wider vulnerabilities of increasing malware attacks. Emerging trends of the malware threats are employing highly sophisticated and dynamic detection avoidance techniques. This has continued to weaken the capacity of existing signature-based detection systems in their protection against new and unknown threats. Thus, the need for effective detection approaches for unknown and novel Android malware has remained a growing challenge in the field of mobile and information security. This study therefore aimed at investigating the best performing machine learning classification algorithm for the anomaly Android malware detection, leveraging on permission-based feature sets, by conduction a performance comparison analysis between six different classification algorithms namely: Naïve Bayes, Simple Logistics, Random Forest, PART, k-Nearest Neighbours (k-NN), and Support Vector Machine (SVM). The Machine learning tool that was used for the preprocessing of the feature sets and the classification processes is WEKA 3.8.2 suite. Findings of the study showed that Random Forest had the best detection result with false alarm rate of 2.2%, accuracy of 97.4%, error rate of 2.6% and ROC Area of 99.6%. The study concluded that, using Android permission features, Random Forest and k-Nearest Neighbours recoded best performances in Android malware detection, followed by Support Vector Machine and Simple Logistics classification algorithms. Partial Decision Tree (PART) performed relatively well, while Naïve Baye recorded the least performance. Consequently, the deployment of Random Forest model and k-NN model are recommended for the development of an anomaly Android malware detection paradigm.

show abstract

Section: The Experimentsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Anomaly Android Malware Detection: A Comparative Analysis of Six Classifiers

Gyunka¹,

Abikoye

Adekunle³

2021

Communications in Computer and Information Science

View full text Add to dashboard Cite

show abstract

“…For the sentiment analyzer, the author considered the TextBlob [9], a customized Word Sense Disambiguation (W-WSD) [10] and The SentiWordNet [11] for comparison. For the classifier, the author leverages the Waikato Environment for Knowledge Analysis (WEKA) [12] software to deliver the classification based on the calculated polarity and subjectivity, using Naïve Bayes Classifier (NBC) [13] and Support Vector Machine (SVM) [14]. The experiment results show that TextBlob and W-WSD are able to achieve better sentiment analysis results than SentiWordNet could under their experiment settings, with accuracies of 76%, 79% and 55% respectively where NBC is the classifier, and 63%, 62% and 53% when SVM is used.…”

Section: Related Work Regarding Twitter Sentiment Analysismentioning

confidence: 99%

A Novel Method for Twitter Sentiment Analysis Based on Attentional-Graph Neural Network

Wang

2020

Information

View full text Add to dashboard Cite

Twitter sentiment analysis is an effective tool for various Twitter-based analysis tasks. However, there is still no neural-network-based research which takes both the tweet-text information and user-connection information into account. To this end, we propose the Attentional-graph Neural Network based Twitter Sentiment Analyzer (AGN-TSA), a Twitter sentiment analyzer based on attentional-graph neural networks. AGN-TSA fuses the tweet-text information and the user-connection information through a three-layered neural structure, which includes a word-embedding layer, a user-embedding layer and an attentional graph network layer. For the training of AGN-TSA, dedicated loss functions are designed for the structural controllability of AGN-TSA network. Experiments based on real-world dataset concerning the 2016 presidential election of America exhibit that AGN-TSA is superior under multiple metrics over several prevailing methods, with a performance boost of over 5%. The empirical settings of parameters are given based on extensive rotation experiments.

show abstract

“…According to Section 2.1, the EMAP features with four attributes were constructed containing 1104 filtered images to represent the spatial information of the Sentinel-2 dataset. To realize the dimensionality reduction of EMAPs, the information gain-based method [78] was used for feature selection, and a ranked list was generated to show superior attributes. This step was conducted using the well-known software Weka [68].…”

Section: Design Of Experimentsmentioning

confidence: 99%

Coastal Wetland Mapping with Sentinel-2 MSI Imagery Based on Gravitational Optimized Multilayer Perceptron and Morphological Attribute Profiles

Zhang

Sun

et al. 2019

Remote Sensing

View full text Add to dashboard Cite

Coastal wetland mapping plays an essential role in monitoring climate change, the hydrological cycle, and water resources. In this study, a novel classification framework based on the gravitational optimized multilayer perceptron classifier and extended multi-attribute profiles (EMAPs) is presented for coastal wetland mapping using Sentinel-2 multispectral instrument (MSI) imagery. In the proposed method, the morphological attribute profiles (APs) are firstly extracted using four attribute filters based on the characteristics of wetlands in each band from Sentinel-2 imagery. These APs form a set of EMAPs which comprehensively represent the irregular wetland objects in multiscale and multilevel. The EMAPs and original spectral features are then classified with a new multilayer perceptron (MLP) classifier whose parameters are optimized by a stability-constrained adaptive alpha for a gravitational search algorithm. The performance of the proposed method was investigated using Sentinel-2 MSI images of two coastal wetlands, i.e., the Jiaozhou Bay and the Yellow River Delta in Shandong province of eastern China. Comparisons with four other classifiers through visual inspection and quantitative evaluation verified the superiority of the proposed method. Furthermore, the effectiveness of different APs in EMAPs were also validated. By combining the developed EMAPs features and novel MLP classifier, complicated wetland types with high within-class variability and low between-class disparity were effectively discriminated. The superior performance of the proposed framework makes it available and preferable for the mapping of complicated coastal wetlands using Sentinel-2 data and other similar optical imagery.

show abstract

An Introduction to the Weka Data Mining System (Abstract Only)

Cited by 27 publications

References 0 publications

Anomaly Android Malware Detection: A Comparative Analysis of Six Classifiers

Anomaly Android Malware Detection: A Comparative Analysis of Six Classifiers

A Novel Method for Twitter Sentiment Analysis Based on Attentional-Graph Neural Network

Coastal Wetland Mapping with Sentinel-2 MSI Imagery Based on Gravitational Optimized Multilayer Perceptron and Morphological Attribute Profiles

Contact Info

Product

Resources

About