Machine Learning is the study of methods for programming computers to learn. Computers are applied to a wide range of tasks, and for most of these it is relatively easy for programmers to design and implement the necessary software. However, there are many tasks for which this is difficult or impossible. These can be divided into four general categories. First, there are problems for which there exist no human experts. For example, in modern automated manufacturing facilities, there is a need to predict machine failures before they occur by analyzing sensor readings. Because the machines are new, there are no human experts who can be interviewed by a programmer to provide the knowledge necessary to build a computer system. A machine learning system can study recorded data and subsequent machine failures and learn prediction rules. Second, there are problems where human experts exist, but where they are unable to explain their expertise. This is the case in many perceptual tasks, such as speech recognition, hand-writing recognition, and natural language understanding. Virtually all humans exhibit expert-level abilities on these tasks, but none of them can describe the detailed steps that they follow as they perform them. Fortunately, humans can provide machines with examples of the inputs and correct outputs for these tasks, so machine learning algorithms can learn to map the inputs to the outputs. Third, there are problems where phenomena are changing rapidly. In finance, for example, people would like to predict the future behavior of the stock market, of consumer purchases, or of exchange rates. These behaviors change frequently, so that even if a programmer could construct a good predictive computer program, it would need to be rewritten frequently. A learning program can relieve the programmer of this burden by constantly modifying and tuning a set of learned prediction rules. Fourth, there are applications that need to be customized for each computer user separately. Consider, for example, a program to filter unwanted electronic mail messages. Different users will need different filters. It is unreasonable to expect each user to program his or her own rules, and it is infeasible to provide every user with a software engineer to keep the rules up-to-date. A machine learning system can learn which mail messages the user rejects and maintain the filtering rules automatically. Machine learning addresses many of the same research questions as the fields of statistics, data mining, and psychology, but with differences of emphasis. Statistics focuses on understanding the phenomena that have generated the data, often with the goal of testing different hypotheses about those phenomena. Data mining seeks to find patterns in the data that are understandable by people. Psychological studies of human learning aspire to understand the mechanisms underlying the various learning behaviors exhibited by people (concept learning, skill acquisition, strategy change, etc.).

Machine learning

In the real world, a realistic setting for computer vision or multimedia recognition problems is that we have some classes containing lots of training data and many classes contain a small amount of training data. Therefore, how to use frequent classes to help learning rare classes for which it is harder to collect the training data is an open question. Learning with Shared Information is an emerging topic in machine learning, computer vision and multimedia analysis. There are different level of components that can be shared during concept modeling and machine learning stages, such as sharing generic object parts, sharing attributes, sharing transformations, sharing regularization parameters and sharing training examples, etc. Regarding the specific methods, multi-task learning, transfer learning and deep learning can be seen as using different strategies to share information. These learning with shared information methods are very effective in solving real-world large-scale problems. This special issue aims at gathering the recent advances in learning with shared information methods and their applications in computer vision and multimedia analysis. Both state-of-the-art works, as well as literature reviews, are welcome for submission. Papers addressing interesting real-world computer vision and multimedia applications are especially encouraged. Topics of interest include, but are not limited to:  • Multi-task learning or transfer learning for large-scale computer vision and multimedia analysis • Deep learning for large-scale computer vision and multimedia analysis • Multi-modal approach for large-scale computer vision and multimedia analysis • Different sharing strategies, e.g., sharing generic object parts, sharing attributes, sharing transformations, sharing regularization parameters and sharing training examples, • Real-world computer vision and multimedia applications based on learning with shared information, e.g., event detection, object recognition, object detection, action recognition, human head pose estimation, object tracking, location-based services, semantic indexing. • New datasets and metrics to evaluate the benefit of the proposed sharing ability for the specific computer vision or multimedia problem. • Survey papers regarding the topic of learning with shared information.  Authors who are unsure whether their planned submission is in scope may contact the guest editors prior to the submission deadline with an abstract, in order to receive feedback.

IEEE transactions on pattern analysis and machine intelligence

Ensemble classification is a data mining approach that utilizes a number of classifiers that work together in order to identify the class label for unlabeled instances. Random forest (RF) is an ensemble classification approach that has proved its high accuracy and superiority. With one common goal in mind, RF has recently received considerable attention from the research community to further boost its performance. In this paper, we look at developments of RF from birth to present. The main aim is to describe the research done to date and also identify potential and future developments to RF. Our approach in this review paper is to take a historical view on the development of this notably successful classification technique. We start with developments that were found before Breiman's introduction of the technique in 2001, by which RF has borrowed some of its components. We then delve into dealing with the main technique proposed by Breiman. A number of developments to enhance the original technique are the...

Random forests: from early developments to recent advancements

Nuclear Instruments and Methods in Physics Research. Section B; Microstructural Characterization of Semi-Interpenetrating Polymer Networks by Positron Lifetime Spectroscopy

Forecasting the direction and trend of stock price is an important task which helps investors to make prudent financial decisions in the stock market Investment in the stock market has a big risk associated with it Minimizing prediction error reduces the investment risk Machine learning (ML) models typically perform better than statistical and econometric models Also, ensemble ML models have been shown in the literature to be able to produce superior performance than single ML models In this work, we compare the effectiveness of tree-based ensemble ML models (Random Forest (RF), XGBoost Classifier (XG), Bagging Classifier (BC), AdaBoost Classifier (Ada), Extra Trees Classifier (ET), and Voting Classifier (VC)) in forecasting the direction of stock price movement Eight different stock data from three stock exchanges (NYSE, NASDAQ, and NSE) are randomly collected and used for the study Each data set is split into training and test set Ten-fold cross validation accuracy is used to evaluate the ML models on the training set In addition, the ML models are evaluated on the test set using accuracy, precision, recall, F1-score, specificity, and area under receiver operating characteristics curve (AUC-ROC) Kendall W test of concordance is used to rank the performance of the tree-based ML algorithms For the training set, the AdaBoost model performed better than the rest of the models For the test set, accuracy, precision, F1-score, and AUC metrics generated results significant to rank the models, and the Extra Trees classifier outperformed the other models in all the rankings

Evaluation of Tree-Based Ensemble Machine Learning Models in Predicting Stock Price Direction of Movement

Leo Breimans Random Forests (RF) is a recent development in tree based classifiers and quickly proven to be one of the most important algorithms in the machine learning literature. It has shown robust and improved results of classifications on standard data sets. Ensemble learning algorithms such as AdaBoost and Bagging have been in active research and shown improvements in classification results for several benchmarking data sets with mainly decision trees as their base classifiers. In this paper we experiment to apply these Meta learning techniques to the random forests. We experiment the working of the ensembles of random forests on the standard data sets available in UCI data sets. We compare the original random forest algorithm with their ensemble counterparts and discuss the results. Keywords— Random Forests [RF], ensembles, UCI. I. PROBLEM DOMAIN ANDOM Forests (RF) [1] are one of the most successful tree based classifiers. It has proven to be fast, robust to noise, and offers possibilities for explanation and visualization of its output. In the random forest method, a large number of classification trees are grown and combined. Statistically speaking two elements serve to obtain a random forest resampling and random split selection. Resampling is done here by sampling multiple times with replacement from the original training data set. Thus in the resulting samples, a certain event may appear several times, and other events not at all. About 2/3 of the data in the training sample are taken for each bootstrap sample and the remaining one-third of the cases are left out of the sample. This oob (out-of-bag) data is used to get a running unbiased estimate of the classification error as trees are added to the forest. It is also used to get estimates of variable importance. The design of random forests is to give the user a good deal of information about the data besides an accurate prediction. Much of this information comes from using the oob cases in the training set that have been left out of the bootstrapped training set. Random split selection is used in each trees growing process. It is computationally effective and offer good prediction performance. It generates an internal unbiased estimate of the generalization. It has an effective method for Manuscript received September 30, 2005. Praveen Boinee is the PhD Student in Computer Science in Udine University, Udine, 33100, Italy (phone: 0039-0432-558231; e-mail: boinee@fisica.uniud.it). Alessandro De Angelis is the Professor in Experimental and Computational Physics at Udine University, Udine, 33100, Italy (e-mail: deangelis@fisica.uniud.it). Gian Luca Foresti is the Professor in Computer Science at Udine University, Udine, Italy (email: foresti@dimi.uniud.it). estimating missing data and maintains accuracy when a large proportion of the data are missing. It generates an internal unbiased estimate of the generalization error as the forest building progresses and thus does not over fit. These capabilities of RF can be extended to unlabeled data, leading to unsupervised clustering, data views and outlier detection. Several authors have noted that constructing ensembles of base learners can significantly improve the performance of learning. Bagging, boosting, are the most popular examples of this methodology. The success of ensemble methods is usually explained with the margin and correlation of base classifiers [13]. To have a good ensemble one needs base classifiers which are diverse (in a sense that they predict differently), yet accurate. The ensemble mechanism which operates on the top of base learners then ensures highly accurate predictions. Here we experiment with random forests as themselves as the base classifiers for making ensembles and test the performance of the model. The ensembles are applied on UCI standard data sets and compared with the original random forest algorithm. The paper is organized as follows. In section II we introduce the decision tress the bases for constructing the random forests. Section III introduces the actual random forests algorithm. Section IV discusses the ensemble learning and making of bagged and boosted random forests. The experiments with UCI data sets are described in section V. Results are discussed in Section VI. II. DECISION TREES – A BASE FOR RANDOM FORESTS The decision-tree representation is the most widely used logic method for efficiently producing classifiers from the data. There is a large number of decision-tree induction algorithms described primarily in the machine-learning and applied-statistics literature. The decision tree algorithm is well known for its robustness and learning efficiency with its learning time complexity of O(nlog2n). The output of the algorithm is a decision tree, which can be easily represented as a set of symbolic rules (IF...THEN). The symbolic rules can be directly interpreted and compared with the existing domain knowledge, providing the useful information for the domain experts. A typical decision-tree learning system adopts a top-down strategy that searches for a solution in a part of the search space. It guarantees that a simple, but not necessarily the simplest, tree will be found. A decision tree consists of nodes that where attributes are tested. The outgoing branches of a node correspond to all the possible outcomes of the test at the Meta Random Forests Praveen Boinee, Alessandro De Angelis, and Gian Luca Foresti R World Academy of Science, Engineering and Technology International Journal of Computer, Electrical, Automation, Control and Information Engineering Vol:2, No:6, 2008 2246 International Scholarly and Scientific Research & Innovation 2(6) 2008 scholar.waset.org/1999.4/3799 In te rn at io na l S ci en ce I nd ex , C om pu te r an d In fo rm at io n E ng in ee ri ng V ol :2 , N o: 6, 2 00 8 w as et .o rg /P ub lic at io n/ 37 99 node. A simple decision tree for classification of samples with two input attributes X and Y is given Fig. 1. Fig. 1 A simple decision tree with the tests on attributes X and Y All samples with feature values X>1 and Y=B belong to Class2, while the samples with values X<1 belong to Class1, whatever the value for feature Y. The samples, at a nonleaf node in the tree structure, are thus partitioned along the branches and each child node gets its corresponding subset of samples. Decision trees that use univariate splits have a simple representational form, making it relatively easy for the user to understand the inferred model; at the same time, they represent a restriction on the expressiveness of the model. In general, any restriction on a particular tree representation can significantly restrict the functional form and thus the approximation power of the model. A well-known treegrowing algorithm for generating decision trees based on univariate splits is Quinlan's ID3 with an extended version called C4.5 [6]. Greedy search methods, which involve growing and pruning decision-tree structures, are typically employed in these algorithms to explore the exponential space of possible models and to remove unnecessary preconditions and duplication. C4.5 applies a divide and conquers strategy to construct the tree. The sets of instances are accompanied by a set of properties. A decision tree is a tree where each node is a test on the values of an attribute, and the leaves represent the class of an instance that satisfies the tests. The tree will return a ‘yes’ or ‘no’ decision when the sets of instances are tested on it. Rules can be derived from the tree by following a path from the root to a leaf and using the nodes along the path as preconditions for the rule, to predict the class at the leaf. For developing random forests, we use the trees that randomly choose a subset of attributes at each mode. III. RANDOM FORESTS A random forest is a classifier consisting of a collection of tree structures classifiers ( ) { } ,... 1 , , = Θ k x h k where the { } k Θ are independent identically distributed random vectors and each tree casts a unit vote for the most popular class at input x. The forest chooses the classification having the most votes over all the trees in the forest. Each tree is grown as follows: 1. If the number of cases in the training set is N, sample N cases at random but with replacement, from the original data. This sample will be the training set for growing the tree. 2. If there are M input variables, a number m<<M is specified such that at each node, m variables are selected at random out of the M and the best split on these m is used to split the node. The value of m is held constant during the forest growing. 3. Each tree is grown to the largest extent possible. There is no pruning. and the overall forest error rate depends on two things: • The correlation between any two trees in the forest. Increasing the correlation increases the forest error rate. • The strength of each individual tree in the forest. A tree with a low error rate is a strong classifier. Increasing the strength of the individual trees decreases the forest error rate. After each tree is built, all of the data are run down the tree, and proximities are computed for each pair of cases. If two cases occupy the same terminal node, their proximity is increased by one. At the end of the run, the proximities are normalized by dividing by the number of trees. Proximities are used in replacing missing data, locating outliers, and producing illuminating low-dimensional views of the data. To formalize the working of the random forests, Let the forest contain K classifier trees ( ) ( ) ( ) x h x h x h K ,..., , 2 1 and the joint classifier be ( ) x h . Each learning instance is represented by an ordered pair (x,y), where each vector of attributes x consists of individual attributes a i Ai ,..., 1 , = (a is the number of attributes) and is labeled with the target value c j y j ,..., 1 , = (c is the number of class values). The correct class is denoted as y, without index

Meta Random Forests

With its diameter of 17m, the MAGIC telescope is the largest Cherenkov detector for gamma ray astrophysics. It is sensitive to photons above an energy of 30 GeV. MAGIC started operations in October 2003 and is currently taking data. This report summarizes its main characteristics, its first results and its potential for physics.

The magic experiment and its first results

Ensemble learning algorithms such as AdaBoost and Bagging have been in active research and shown improvements in classification results for several benchmarking data sets with mainly decision trees as their base classifiers. In this paper we experiment to apply these Meta learning techniques with classifiers such as random forests, neural networks and support vector machines. The data sets are from MAGIC, a Cherenkov telescope experiment. The task is to classify gamma signals from overwhelmingly hadron and muon signals representing a rare class classification problem. We compare the individual classifiers with their ensemble counterparts and discuss the results. WEKA a wonderful tool for machine learning has been used for making the experiments. Keywords—Ensembles, WEKA, Neural networks [NN], Support Vector Machines [SVM], Random Forests [RF].

Ensembling Classifiers – An Application toImage Data Classification from Cherenkov Telescope Experiment

Ensembling Classifiers - An application to image data classification from Cherenkov telescope experiment.

Neural networks have proved to be versatile and robust for particle separation in many experiments related to particle astrophysics. We apply these techniques to separate gamma rays from hadrons for the MAGIC Cerenkov Telescope. Two types of neural network architectures have been used for the classification task: one is the MultiLayer Perceptron (MLP) based on supervised learning, and the other is the Self-Organising Tree Algorithm (SOTA), which is based on unsupervised learning. We propose a new architecture by combining these two neural networks types to yield better and faster classification results for our classification problem.

pdf/neural-networks-for-gamma-hadron-separation-in-magic-shjfwlb9lr.pdf

Neural networks for gamma-hadron separation in magic

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