Millions Of Instances Research Articles

Exploring and exploiting a historical corpus for Arabic

This paper presents a historical Arabic corpus named HAC. At this early embryonic stage of the project, we report about the design, the architecture and some of the experiments which we have conducted on HAC. The corpus, and accordingly the search results, will be represented using a primary XML exchange format. This will serve as an intermediate exchange tool within the project and will allow the user to process the results offline using some external tools. HAC is made up of Classical Arabic texts that cover 1600 years of language use; the Quranic text, Modern Standard Arabic texts, as well as a variety of monolingual Arabic dictionaries. The development of this historical corpus assists linguists and Arabic language learners to effectively explore, understand, and discover interesting knowledge hidden in millions of instances of language use. We used techniques from the field of natural language processing to process the data and a graph-based representation for the corpus. We provided researchers with an export facility to render further linguistic analysis possible.

Evolutionary Feature Selection for Big Data Classification: A MapReduce Approach

Nowadays, many disciplines have to deal with big datasets that additionally involve a high number of features. Feature selection methods aim at eliminating noisy, redundant, or irrelevant features that may deteriorate the classification performance. However, traditional methods lack enough scalability to cope with datasets of millions of instances and extract successful results in a delimited time. This paper presents a feature selection algorithm based on evolutionary computation that uses the MapReduce paradigm to obtain subsets of features from big datasets. The algorithm decomposes the original dataset in blocks of instances to learn from them in the map phase; then, the reduce phase merges the obtained partial results into a final vector of feature weights, which allows a flexible application of the feature selection procedure using a threshold to determine the selected subset of features. The feature selection method is evaluated by using three well-known classifiers (SVM, Logistic Regression, and Naive Bayes) implemented within the Spark framework to address big data problems. In the experiments, datasets up to 67 millions of instances and up to 2000 attributes have been managed, showing that this is a suitable framework to perform evolutionary feature selection, improving both the classification accuracy and its runtime when dealing with big data problems.

MRPR: A MapReduce solution for prototype reduction in big data classification

In the era of big data, analyzing and extracting knowledge from large-scale data sets is a very interesting and challenging task. The application of standard data mining tools in such data sets is not straightforward. Hence, a new class of scalable mining method that embraces the huge storage and processing capacity of cloud platforms is required. In this work, we propose a novel distributed partitioning methodology for prototype reduction techniques in nearest neighbor classification. These methods aim at representing original training data sets as a reduced number of instances. Their main purposes are to speed up the classification process and reduce the storage requirements and sensitivity to noise of the nearest neighbor rule. However, the standard prototype reduction methods cannot cope with very large data sets. To overcome this limitation, we develop a MapReduce-based framework to distribute the functioning of these algorithms through a cluster of computing elements, proposing several algorithmic strategies to integrate multiple partial solutions (reduced sets of prototypes) into a single one. The proposed model enables prototype reduction algorithms to be applied over big data classification problems without significant accuracy loss. We test the speeding up capabilities of our model with data sets up to 5.7 millions of instances. The results show that this model is a suitable tool to enhance the performance of the nearest neighbor classifier with big data.

LiNearN: A new approach to nearest neighbour density estimator

Despite their wide spread use, nearest neighbour density estimators have two fundamental limitations: O(n2) time complexity and O(n) space complexity. Both limitations constrain nearest neighbour density estimators to small data sets only. Recent progress using indexing schemes has improved to near linear time complexity only.We propose a new approach, called LiNearN for Linear time Nearest Neighbour algorithm, that yields the first nearest neighbour density estimator having O(n) time complexity and constant space complexity, as far as we know. This is achieved without using any indexing scheme because LiNearN uses a subsampling approach for which the subsample values are significantly less than the data size. Like existing density estimators, our asymptotic analysis reveals that the new density estimator has a parameter to trade off between bias and variance. We show that algorithms based on the new nearest neighbour density estimator can easily scale up to data sets with millions of instances in anomaly detection and clustering tasks.

A scalable approach to simultaneous evolutionary instance and feature selection

An enormous amount of information is continually being produced in current research, which poses a challenge for data mining algorithms. Many of the problems in extremely active research areas, such as bioinformatics, security and intrusion detection and text mining, involve large or enormous datasets. These datasets pose serious problems for many data mining algorithms.One method to address very large datasets is data reduction. Among the most useful data reduction methods is simultaneous instance and feature selection. This method achieves a considerable reduction in the training data while maintaining, or even improving, the performance of the data-mining algorithm. However, it suffers from a high degree of scalability problems, even for medium-sized datasets. In this paper, we propose a new evolutionary simultaneous instance and feature selection algorithm that is scalable to millions of instances and thousands of features.This proposal is based on the divide-and-conquer principle combined with bookkeeping. The divide-and-conquer principle allows the execution of the algorithm in linear time. Furthermore, the proposed method is easy to implement using a parallel environment and can work without loading the entire dataset into memory.Using 50 medium-sized datasets, we will demonstrate our method’s ability to match the results of state-of-the-art instance and feature selection methods while significantly reducing the time requirements. Using 13 very large datasets, we will demonstrate the scalability of our proposal to millions of instances and thousands of features.

OligoIS: Scalable Instance Selection for Class-Imbalanced Data Sets

In current research, an enormous amount of information is constantly being produced, which poses a challenge for data mining algorithms. Many of the problems in extremely active research areas, such as bioinformatics, security and intrusion detection, or text mining, share the following two features: large data sets and class-imbalanced distribution of samples. Although many methods have been proposed for dealing with class-imbalanced data sets, most of these methods are not scalable to the very large data sets common to those research fields. In this paper, we propose a new approach to dealing with the class-imbalance problem that is scalable to data sets with many millions of instances and hundreds of features. This proposal is based on the divide-and-conquer principle combined with application of the selection process to balanced subsets of the whole data set. This divide-and-conquer principle allows the execution of the algorithm in linear time. Furthermore, the proposed method is easy to implement using a parallel environment and can work without loading the whole data set into memory. Using 40 class-imbalanced medium-sized data sets, we will demonstrate our method's ability to improve the results of state-of-the-art instance selection methods for class-imbalanced data sets. Using three very large data sets, we will show the scalability of our proposal to millions of instances and hundreds of features.

Fast and accurate text classification via multiple linear discriminant projections

Abstract.Support vector machines (SVMs) have shown superb performance for text classification tasks. They are accurate, robust, and quick to apply to test instances. Their only potential drawback is their training time and memory requirement. For n training instances held in memory, the best-known SVM implementations take time proportional to n a, where a is typically between 1.8 and 2.1. SVMs have been trained on data sets with several thousand instances, but Web directories today contain millions of instances that are valuable for mapping billions of Web pages into Yahoo!-like directories. We present SIMPL, a nearly linear-time classification algorithm that mimics the strengths of SVMs while avoiding the training bottleneck. It uses Fisher's linear discriminant, a classical tool from statistical pattern recognition, to project training instances to a carefully selected low-dimensional subspace before inducing a decision tree on the projected instances. SIMPL uses efficient sequential scans and sorts and is comparable in speed and memory scalability to widely used naive Bayes (NB) classifiers, but it beats NB accuracy decisively. It not only approaches and sometimes exceeds SVM accuracy, but also beats the running time of a popular SVM implementation by orders of magnitude. While describing SIMPL, we make a detailed experimental comparison of SVM-generated discriminants with Fisher's discriminants, and we also report on an analysis of the cache performance of a popular SVM implementation. Our analysis shows that SIMPL has the potential to be the method of choice for practitioners who want the accuracy of SVMs and the simplicity and speed of naive Bayes classifiers.

Production and Profits

T HE influence of production on profits has always been obvious and methods of reducing costs by increasing production have received very general attention since Mr. F. W. Taylor's careful analysis of the factor of overhead expense; but the influence of profits on production, though always a very real factor in industrial engineering, becomes obvious only under special conditions. In England during the war, in those cases where the earnings of the wage worker were increased to a point sufficient to fulfill all his ordinary desires, the final disposition of the profits arising in connection with his efforts became a very real force in spurring him on or holding him back. In spite of possibilities of increasing his own earnings, a normal human being will find his efforts lessened by the knowledge that he is also increasing an already liberal return to some absent stockholder; or will be moved to demand a larger share for himself and to stop work in support of that demand, and even to relax his efforts after the demand has been granted. On the other hand, there are millions of instances the world over of almost superhuman energy given forth week in and week out for four long years where the sole advantage beyond the pay envelope accrued. to the worker's country and its allies. American business men have taught their fellows that each business venture is an undertaking for profits and that they cannot be surprised if the profits of a business and the people to whom they go are subjects of real interest to every employee. Wherever, to the minds of employees, the distribution of profits has the flavor of justice, of a reasonable connection of ause and effect, of reward and dessert, the great majority of them feel a mild sense of satisfaction. None but that almost negligible percentage of true radicals, which is always with us, begrudges the reward of an Edison, a Vail or a Henry Ford, nor the reasonable reward of those who have risked their money in useful ventures. However, unr st begins and restless propositions arise where increased profits are known to be going to those who are inactive and w ose public service is invisible. Today, when production to support life is as seriously needed by the world s was production to destroy life during the war, this influence of the distribution of profits upon the primary agent of production is a challenge to all men interested in the profession of management. When we have to face any of the mechanical problems which the industrial process presents to us by the hundred thousand, we know that a careful analysis of causes and effects of the elemental forces and their interrelations is essential and we waste no time in cursing the crotchet of the machine or the crankiness of the material, but set to work finding the sources of trouble. Not so many managers have yet learned the value of maintaining the same clear-sighted, common-sense attitude when the problem lies within the field of labor. Yet it is obvious that the forces at work, brewing and stewing among a thousand employees, are as vital or as fatal as those in any electric furnace. I know many