Data Mining: Practical Machine Learning Tools and Techniques, 3/E

By Ian H. Witten, Eibe Frank, Mark A. Hall. 2014

1,298 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/0fa45cfa88ee9ac93cb01ec159ad8713d0e32d93

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Citations in the corpus

Activity recognition using cell phone accelerometers (2011, methodology): “Once the data set was prepared, we used three classification techniques from the WEKA data mining suite [20] to induce models for predicting the user activities: decision trees (J48), logistic regression and multilayer neural networks.”
Classification of Cancer Data: Analyzing Gene Expression Data Using a Fuzzy Decision Tree Algorithm (2018, methodology): “The classical decision tree algorithm used for comparison is WEKA’s J48 decision tree implementation (Witten et al., 2011).”
Educational data mining: A survey and a data mining-based analysis of recent works (2014, background): “Worsley and Blikstein (2011), id 113, propose using student speech and drawings to decipher meaningful markers of expertise in an automated and natural fashion.”
Fusion of Nonintrusive Environmental Sensors for Occupancy Detection in Smart Homes (2018, methodology): “In multiclass applications, the class value with the highest probability is predicted [22].”
A Hidden Markov Model to detect coded information islands in free text (2013, methodology): “To evaluate the effectiveness of our approach we adopt the Precision and Recall metrics, known respectively also as Positive Predictive Value (PPV) and Sensitivity [24].”
A review of automatic selection methods for machine learning algorithms and hyper-parameter values (2016, methodology): “Auto-WEKA runs on one computer and is based on Weka (Witten et al. 2011 Using an approach similar to that in Auto-WEKA, Komer et al. developed the software hyperopt-sklearn (Komer et al. 2014; Bergstra et al. 2013), which automatically selects machine learning algorithms and hyper-parameter values…”
Towards human-guided machine learning (2019, methodology): “Auto-WEKA [1] combines automated model selection with (hyper)parameter optimization using the machine learning functions or primitives provided by the popular WEKA toolkit [7].”
Machine Learning and Data Mining Methods in Diabetes Research (2017, background): “The advantages of feature selection are many and relate to different aspects of data analysis, such as better visualization and understanding of data, reduction of computational time and duration of analysis, and better prediction accuracy [12,13].”
A Survey of Payload-Based Traffic Classification Approaches (2014, background): “The supervised scheme operates under supervision by being provided with the actual outcome for each of the training records, whereas the unsupervised scheme is used without any prior information to group records that seem to fall naturally together [17].”
Reliable Accuracy Estimates from k-Fold Cross Validation (2020, background): “For example, 10-fold cross validation is recommended to perform ten times in [3], [4].”
Educational data mining: prediction of students' academic performance using machine learning algorithms (2022, background): “It is the field of discovering new and potentially useful information or meaningful results from big data (Witten et al., 2011).”
Machine learning for prediction of all-cause mortality in patients with suspected coronary artery disease: a 5-year multicentre prospective registry analysis (2016, methodology): “Cross-validation The performance and general error estimation of the entire ML process (feature selection and LogitBoost) was assessed using stratified 10-fold cross-validation which is currently the preferred technique in data mining.(13,17) The main advantages of this validation technique compared”
Fake Twitter followers detection by denoising autoencoder (2017, background): “An autoencoder, also known as a replicator neural network, is a particular kind of artificial neural network, precisely a feed-forward multi-layer perceptron [28], having the same number of nodes, corresponding to the number of features of the data, both in the input and in the output layers.”
A Survey of Data Mining and Machine Learning Methods for Cyber Security Intrusion Detection (2016, methodology): “Naïve Bayes classifiers [89] are simple probabilistic classifiers applying the Bayes theorem.”
A network intrusion detection system based on a Hidden Naïve Bayes multiclass classifier (2012, methodology): “In total, 42 model combinations were tested using the Weka tool (Witten, Frank, & Hall, 2011).”
Survey on Fall Detection and Fall Prevention Using Wearable and External Sensors (2014, methodology): “The stopping criteria is met when all of the instances in the branch have the same class or there are no more attributes left [18].”
Data Center Energy Consumption Modeling: A Survey (2016, methodology): “They used the M5P algorithm [367] for calculating the expected CPU and I/O usage [364][368].”
Ordinal Regression Methods: Survey and Experimental Study (2016, methodology): “LEARNING to classify or to predict numerical values from prelabelled patterns is one of the central research topics in machine learning and data mining [1], [2], [3].”