DATA MINING and MACHINE LEARNING. CLASSIFICATION PREDICTIVE TECHNIQUES: NAIVE BAYES, NEAREST NEIGHBORS and NEURAL NETWORKS: Examples with MATLAB
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DATA MINING and MACHINE LEARNING. CLASSIFICATION PREDICTIVE TECHNIQUES - César Pérez López
DATA MINING AND MACHINE LEARNING. CLASSIFICATION PREDICTIVE TECHNIQUES: NAIVE BAYES, NEAREST NEIGHBORS AND NEURAL NETWORKS
Examples with MATLAB
César Pérez López
CONTENTS
DATA MINING TECHNIQUES
1.1 DATA MINING AND MACHNE LEARNING INTRODUCTION
1.1.1 Data Mining and Machine Learning Techniques with Matlab
1.1.2 Train Classification Models in Classification Learner App
1.1.3 Train Regression Models in Regression Learner App
1.1.4 Train Neural Networks for Deep Learning
PREDICTIVE TECHNIQUES. CLASSIFICATION MODELS WITH CLASSIFICATION LEARNER
2.1 Train Classification Models in Classification Learner App
2.1.1 What Is Supervised Machine Learning?
2.1.2 Automated Classifier Training
2.1.3 Manual Classifier Training
2.1.4 Parallel Classifier Training
2.1.5 Compare and Improve Classification Models
2.2 Choose Classifier Options
2.2.1 Choose a Classifier Type
2.2.2 Decision Trees
2.2.3 Discriminant Analysis
2.2.4 Logistic Regression
2.2.5 Support Vector Machines
2.2.6 Nearest Neighbor Classifiers
2.2.7 Ensemble Classifiers
2.3 Assess Classifier Performance in Classification Learner
2.3.1 Check Performance in the History List
2.3.2 Plot Classifier Results
2.3.3 Check Performance Per Class in the Confusion Matrix
2.3.4 Check the ROC Curve
PREDICTIVE TECHNIQUES. CLASSIFICATION WITH NAIVE BAYES
3.1 Naive Bayes Classification
3.1.1 Supported Distributions
3.2 Functions
3.2.1 fitcnb
3.2.2 predict
3.2.3 templateNaiveBayes
PREDICTIVE TECHNIQUES. CLASSIFICATION WITH NEAREST NEIGHBORS. KNN CLASSIFIERS
4.1 Classification Using Nearest Neighbors
4.1.1 Pairwise Distance Metrics
4.1.2 k-Nearest Neighbor Search and Radius Search
4.1.3 Classify Query Data
4.1.4 Find Nearest Neighbors Using a Custom Distance Metric
4.2 K-Nearest Neighbor Classification for Supervised Learning
4.2.1 Construct KNN Classifier
4.2.2 Examine Quality of KNN Classifier
4.2.3 Predict Classification Using KNN Classifier
4.2.4 Modify KNN Classifier
4.3 Nearest Neighbors FUNCTIONS
4.3.1 ExhaustiveSearcher
4.3.2 KDTreeSearcher
4.3.3 createns
PREDICTIVE TECHNIQUES. PATTERN RECOGNITION with a Neural Network
5.1 NEURAL NETWORK TOOLBOX (DEEP LEARNING TOOLBOX)
5.2 Using Neural Network Toolbox
5.3 Automatic Script Generation
5.4 Neural Network Toolbox Applications
5.5 Neural Network Design Steps
5.6 INTRODUCTION TO PATTERNS RECOGNITION WITH NEURAL NETWORKS
5.7 Using the Neural Network Pattern Recognition Tool
5.8 Using Command-Line Functions
PREDICTIVE TECHNIQUES. FunctionS FOR PATTERN RECOGNITION AND CLASSIFICATION WITH NEURAL NETWORKS
6.1 INTRODUCTION
6.2 view NEURAL NETWORK
6.3 Pattern Recognition and Learning Vector Quantization
6.3.1 Pattern recognition network: patternnet
6.3.2 Learning vector quantization neural network: lvqnet
6.4 Training Options and Network Performance
6.4.1 Receiver operating characteristic: roc
6.4.2 Plot receiver operating characteristic: plotroc
6.4.3 Plot classification confusion matrix: plotconfusion
6.4.4 Neural network performance: crossentropy
PREDICTIVE TECHNIQUES. CLASSIFICATION WITH NEURAL NETWORKS. EXAMPLES
7.1 Crab Classification
7.1.1 Why Neural Networks?
7.1.2 Preparing the Data
7.1.3 Building the Neural Network Classifier
7.1.4 Testing the Classifier
7.2 Wine Classification
7.2.1 The Problem: Classify Wines
7.2.2 Why Neural Networks?
7.2.3 Preparing the Data
7.2.4 Pattern Recognition with a Neural Network
7.2.5 Testing the Neural Network
7.3 Cancer Detection
7.3.1 Formatting the Data
7.3.2 Ranking Key Features
7.3.3 Classification Using a Feed Forward Neural Network
7.4 Character Recognition
7.4.1 Creating the First Neural Network
7.4.2 Training the first Neural Network
7.4.3 Training the Second Neural Network
7.4.4 Testing Both Neural Networks
NEURAL NETWORK DESIGN PROCESS
8.1 INTRODUCTION
8.2 Four Levels of Neural Network Design
8.3 Neural Network Architectures
8.3.1 One Layer of Neurons
8.3.2 Multiple Layers of Neurons
8.3.3 Input and Output Processing Functions
8.4 Multilayer Neural Networks and Backpropagation Training
8.5 Multilayer Neural Network Architecture
8.5.1 Neuron Model (logsig, tansig, purelin)
8.5.2 Feedforward Neural Network
8.6 Understanding Neural Networks Toolbox (Deep Learning Toolbox from version 18) Data Structures
8.6.1 Simulation with Concurrent Inputs in a Static Network
8.6.2 Simulation with Sequential Inputs in a Dynamic Network
8.6.3 Simulation with Concurrent Inputs in a Dynamic Network
NEURAL NETWORK ARCHITECTURE: PERCEPTRON NEURAL NETWORKS
9.1 INTRODUCTION
9.2 Neuron Model
9.3 Perceptron Architecture
9.4 Create a Perceptron
9.5 Perceptron Learning Rule (learnp)
9.6 Training (train)
9.7 Limitations and Cautions
9.8 PERCEPTRON EXAMPLES
9.8.1 Classification with a 2-Input Perceptron
9.8.2 Outlier Input Vectors
9.8.3 Normalized Perceptron Rule
9.8.4 Linearly Non-separable Vectors
NEURAL NETWORK ARCHITECTURE: RADIAL BASIS Neural NetworkS
10.1 RADIAL BASIS FUNCTION NETWORK
10.2 Neuron Model
10.3 Network Architecture
10.4 Exact Design (newrbe)
10.5 More Efficient Design (newrb)
10.6 Radial Basis EXAMPLES
10.6.1 Radial Basis Approximation
10.6.2 Radial Basis Underlapping Neurons
10.6.3 GRNN Function Approximation
10.6.4 PNN Classification
DATA MINING TECHNIQUES
The availability of large volumes of data and the generalized use of computer tools has transformed research and data analysis, orienting it towards certain specialized techniques encompassed under the generic name of Analytics that includes Multivariate Data Analysis (MDA), Data Mining, Machine Learning and other Business Intelligence techniques.
Data Mining and Machine Learning can be defined as a process of discovering new and significant relationships, patterns and trends when examining large amounts of data. The techniques of Data Mining and Machine Learning pursue the automatic discovery of the knowledge contained in the information stored in an orderly manner in large databases. These techniques aim to discover patterns, profiles and trends through the analysis of data using advanced statistical techniques of multivariate data analysis.
The goal is to allow the researcher-analyst to find a useful solution to the problem raised through a better understanding of the existing data.
Data Mining and Machine Learning uses two types of techniques: predictive techniques (supervised techniques), which trains a model on known input and output data so that it can predict future outputs, and descriptive techniques (unsupervised techniques), which finds hidden patterns or intrinsic structures in input data.
The aim of predictive techniques is to build a model that makes predictions based on evidence in the presence of uncertainty. A predictive algorithm takes a known set of input data and known responses to the data (output) and trains a model to generate reasonable predictions for the response to new data. Predictive techniques uses classification and regression techniques to develop predictive models.
Classification techniques predict categorical responses, for example, whether an email is genuine or spam, or whether a tumor is cancerous or benign. Classification models classify input data into categories. Typical applications include medical imaging, image and speech recognition, and credit scoring. This book develops predictive classification techniques.
Regression techniques predict continuous responses, for example, changes in temperature or fluctuations in power demand. Typical applications include electricity load forecasting and algorithmic trading.
Descriptive techniques finds hidden patterns or intrinsic structures in data. It is used to draw inferences from datasets consisting of input data without labeled responses. Clustering is the most common descriptive technique. It is used for exploratory data analysis to find hidden patterns or groupings in data. Applications for clustering include gene sequence analysis, market research, and object recognition.
MATLAB provides tools to help you try out a variety of Data Mining and Machine Learning models and choose the best. To find MATLAB apps and functions to help you solve Data Mining tasks, consult the following table. Some Data Mining tasks are made easier by using apps, and others use command-line features.
The following systematic Data Mining nd Machine Learning workflow can help you tackle Data Mining and Machine Learning challenges. You can complete the entire workflow in MATLAB.
Descripción: http://es.mathworks.com/help/stats/machinelearningoverviewworkflow.jpgTo integrate the best trained model into a production system, you can deploy Statistics and Machine Learning Toolbox machine learning models using MATLAB Compiler. For many models, you can generate C-code for prediction using MATLAB Coder.
Use the Classification Learner app to train models to classify data using predictive Data Miming techniques. The app lets you explore predictive Data Mining interactively using various classifiers.
Automatically train a selection of models and help you choose the best model. Model types include decision trees, discriminant analysis, support vector machines, logistic regression, nearest neighbors, and ensemble classification.
Explore your data, select features, and visualize results.
Export models to the workspace to make predictions with new data.
Generate MATLAB code from the app to create scripts, train with new data, work with huge data sets, or modify the code for further analysis.
By default, the app protects against overfitting by applying cross-validation. Alternatively, you can choose holdout validation.
Descripción: http://es.mathworks.com/help/stats/mlapp_overview.pngFor more options, you can use the command-line interface. See Classification.
Use the Regression Learner app to train models to predict continuous data using predicte Data Mining. The app lets you explore predictive Data Mininig techniques interactively using various regression models.
Automatically train a selection of models and help you choose the best model. Model types include linear regression models, regression trees, Gaussian process regression models, support vector machines, and ensembles of regression trees.
Explore your data, select features, and visualize results.
Export models to the workspace to make predictions with new data.
Generate MATLAB code from the app to create scripts, train with new data, work with huge data sets, or modify the code for further analysis.
By default, the app protects against overfitting by applying cross-validation. Alternatively, you can choose holdout validation.
Descripción: http://es.mathworks.com/help/stats/regressionlearneroverview17a.pngNeural Network Toolbox (Deep Learning Toolbox from version 18) enables you to perform deep learning with convolutional neural networks for classification, regression, feature extraction, and transfer learning. The toolbox provides simple MATLAB commands for creating and interconnecting the layers of a deep neural network. Examples and pretrained networks make it easy to use MATLAB for deep learning, even without extensive knowledge of advanced computer vision algorithms or neural networks.
.
PREDICTIVE TECHNIQUES. CLASSIFICATION MODELS WITH CLASSIFICATION LEARNER
You can use Classification Learner to train models to classify data. Using this app, you can explore supervised machine learning using various classifiers. You can explore your data, select features, specify validation schemes, train models, and assess results. You can perform automated training to search for the best classification model type, including decision trees, discriminant analysis, support vector machines, logistic regression, nearest neighbors, and ensemble classification.
Perform supervised machine learning by supplying a known set of input data (observations or examples) and known responses to the data (i.e., labels or classes). Use the data to train a model that generates predictions for the response to new data. To use the model with new data, or to learn about programmatic classification, you can export the model to the workspace or generate MATLAB® code to recreate the trained model.
Descripción: http://es.mathworks.com/help/stats/supervised_learning_model.pngGet started by training a selection of model types.
You can use Classification Learner to automatically train a selection of different classification models on your data.
Get started by automatically training multiple models