Datasets used in machine learning
by Krishna Choppella, Dr. Uday Kamath, Boštjan Kaluža, Jennifer L. Reese, Richard M
Machine Learning: End-to-End guide for Java developers
- Machine Learning: End-to-End guide for Java developers
- Table of Contents
- Machine Learning: End-to-End guide for Java developers
- Credits
- Preface
- 1. Module 1
- 1. Getting Started with Data Science
- Problems solved using data science
- Understanding the data science problem - solving approach
- Acquiring data for an application
- The importance and process of cleaning data
- Visualizing data to enhance understanding
- The use of statistical methods in data science
- Machine learning applied to data science
- Using neural networks in data science
- Deep learning approaches
- Performing text analysis
- Visual and audio analysis
- Improving application performance using parallel techniques
- Assembling the pieces
- Summary
- 2. Data Acquisition
- 3. Data Cleaning
- 4. Data Visualization
- 5. Statistical Data Analysis Techniques
- 6. Machine Learning
- 7. Neural Networks
- 8. Deep Learning
- 9. Text Analysis
- 10. Visual and Audio Analysis
- 11. Mathematical and Parallel Techniques for Data Analysis
- 12. Bringing It All Together
- 1. Getting Started with Data Science
- 2. Module 2
- 1. Applied Machine Learning Quick Start
- 2. Java Libraries and Platforms for Machine Learning
- 3. Basic Algorithms – Classification, Regression, and Clustering
- 4. Customer Relationship Prediction with Ensembles
- 5. Affinity Analysis
- 6. Recommendation Engine with Apache Mahout
- 7. Fraud and Anomaly Detection
- 8. Image Recognition with Deeplearning4j
- 9. Activity Recognition with Mobile Phone Sensors
- 10. Text Mining with Mallet – Topic Modeling and Spam Detection
- 11. What is Next?
- A. References
- 3. Module 3
- 1. Machine Learning Review
- Machine learning – history and definition
- What is not machine learning?
- Machine learning – concepts and terminology
- Machine learning – types and subtypes
- Datasets used in machine learning
- Machine learning applications
- Practical issues in machine learning
- Machine learning – roles and process
- Machine learning – tools and datasets
- Summary
- 2. Practical Approach to Real-World Supervised Learning
- Formal description and notation
- Data transformation and preprocessing
- Feature relevance analysis and dimensionality reduction
- Model building
- Model assessment, evaluation, and comparisons
- Case Study – Horse Colic Classification
- Summary
- References
- 3. Unsupervised Machine Learning Techniques
- Issues in common with supervised learning
- Issues specific to unsupervised learning
- Feature analysis and dimensionality reduction
- Clustering
- Outlier or anomaly detection
- Real-world case study
- Summary
- References
- 4. Semi-Supervised and Active Learning
- Semi-supervised learning
- Active learning
- Case study in active learning
- Summary
- References
- 5. Real-Time Stream Machine Learning
- Assumptions and mathematical notations
- Basic stream processing and computational techniques
- Concept drift and drift detection
- Incremental supervised learning
- Incremental unsupervised learning using clustering
- Modeling techniques
- Partition based
- Hierarchical based and micro clustering
- Density based
- Grid based
- Validation and evaluation techniques
- Key issues in stream cluster evaluation
- Evaluation measures
- Cluster Mapping Measures (CMM)
- V-Measure
- Other external measures
- Unsupervised learning using outlier detection
- Case study in stream learning
- Summary
- References
- 6. Probabilistic Graph Modeling
- Probability revisited
- Graph concepts
- Bayesian networks
- Representation
- Inference
- Learning
- Learning parameters
- Learning structures
- Measures to evaluate structures
- Methods for learning structures
- Constraint-based techniques
- Search and score-based techniques
- 7. Deep Learning
- Multi-layer feed-forward neural network
- Inputs, neurons, activation function, and mathematical notation
- Multi-layered neural network
- Structure and mathematical notations
- Activation functions in NN
- Training neural network
- Empirical risk minimization
- Parameter initialization
- Loss function
- Gradients
- Feed forward and backpropagation
- How does it work?
- Regularization
- L2 regularization
- L1 regularization
- Limitations of neural networks
- Deep learning
- Building blocks for deep learning
- Rectified linear activation function
- Restricted Boltzmann Machines
- Autoencoders
- Unsupervised pre-training and supervised fine-tuning
- Deep feed-forward NN
- Deep Autoencoders
- Deep Belief Networks
- Deep learning with dropouts
- Sparse coding
- Convolutional Neural Network
- CNN Layers
- Recurrent Neural Networks
- Building blocks for deep learning
- Case study
- Summary
- References
- 8. Text Mining and Natural Language Processing
- NLP, subfields, and tasks
- Text categorization
- Part-of-speech tagging (POS tagging)
- Text clustering
- Information extraction and named entity recognition
- Sentiment analysis and opinion mining
- Coreference resolution
- Word sense disambiguation
- Machine translation
- Semantic reasoning and inferencing
- Text summarization
- Automating question and answers
- Issues with mining unstructured data
- Text processing components and transformations
- Topics in text mining
- Tools and usage
- Summary
- References
- NLP, subfields, and tasks
- 9. Big Data Machine Learning – The Final Frontier
- What are the characteristics of Big Data?
- Big Data Machine Learning
- Batch Big Data Machine Learning
- Case study
- Business problem
- Machine Learning mapping
- Data collection
- Data sampling and transformation
- Spark MLlib as Big Data Machine Learning platform
- A. Linear Algebra
- B. Probability
- D. Bibliography
- Index
- Empirical risk minimization
- Multi-layer feed-forward neural network
- Modeling techniques
- 1. Machine Learning Review
To learn from data, we must be able to understand and manage data in all forms. Data originates from many different sources, and consequently, datasets may differ widely in structure or have little or no structure at all. In this section, we present a high-level classification of datasets with commonly occurring examples.
Based on their structure, or the lack thereof, datasets may be classified as containing the following:
- Structured data: Datasets with structured data are more amenable to being used as input to most machine learning algorithms. The data is in the form of records or rows following a well-known format with features that are either columns in a table or fields delimited by separators or tokens. There is no explicit relationship between the records or instances. The dataset is available chiefly in flat files or relational databases. The records of financial transactions at a bank shown in the following figure are an example of structured data:
Financial card transactional data with labels of fraud
- Transaction or market data: This is a special form of structured data where each entry corresponds to a collection of items. Examples of market datasets are the lists of grocery items purchased by different customers or movies viewed by customers, as shown in the following table:
Market dataset for items bought from grocery store
- Unstructured data: Unstructured data is normally not available in well-known formats, unlike structured data. Text data, image, and video data are different formats of unstructured data. Usually, a transformation of some form is needed to extract features from these forms of data into a structured dataset so that traditional machine learning algorithms can be applied.
Sample text data, with no discernible structure, hence unstructured. Separating spam from normal messages (ham) is a binary classification problem. Here true positives (spam) and true negatives (ham) are distinguished by their labels, the second token in each instance of data. SMS Spam Collection Dataset (UCI Machine Learning Repository), source: Tiago A. Almeida from the Federal University of Sao Carlos.
- Sequential data: Sequential data have an explicit notion of "order" to them. The order can be some relationship between features and a time variable in time series data, or it can be symbols repeating in some form in genomic datasets. Two examples of sequential data are weather data and genomic sequence data. The following figure shows the relationship between time and the sensor level for weather:
Time series from sensor data
Three genomic sequences are taken into consideration to show the repetition of the sequences
CGGGTandTTGAAAGTGGTGin all three genomic sequences:
Genomic sequences of DNA as a sequence of symbols.
- Graph data: Graph data is characterized by the presence of relationships between entities in the data to form a graph structure. Graph datasets may be in a structured record format or an unstructured format. Typically, the graph relationship has to be mined from the dataset. Claims in the insurance domain can be considered structured records containing relevant claim details with claimants related through addresses, phone numbers, and so on. This can be viewed in a graph structure. Using the World Wide Web as an example, we have web pages available as unstructured data containing links, and graphs of relationships between web pages that can be built using web links, producing some of the most extensively mined graph datasets today:
Insurance claim data, converted into a graph structure showing the relationship between vehicles, drivers, policies, and addresses
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