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

Data Mining Practical Machine Learning Tools and Techniques

https://scholarscompass.vcu.edu/cgi/viewcontent.cgi?article=1038&context=cmsc_pubs

Ensemble learning for data stream analysis

Opcode sequences as representation of executables for data-mining-based unknown malware detection

Abstract Class imbalance is often a problem in various real-world data sets, where one class (i.e. the minority class) contains a small number of data points and the other (i.e. the majority class) contains a large number of data points. It is notably difficult to develop an effective model using current data mining and machine learning algorithms without considering data preprocessing to balance the imbalanced data sets. Random undersampling and oversampling have been used in numerous studies to ensure that the different classes contain the same number of data points. A classifier ensemble (i.e. a structure containing several classifiers) can be trained on several different balanced data sets for later classification purposes. In this paper, we introduce two undersampling strategies in which a clustering technique is used during the data preprocessing step. Specifically, the number of clusters in the majority class is set to be equal to the number of data points in the minority class. The first strategy uses the cluster centers to represent the majority class, whereas the second strategy uses the nearest neighbors of the cluster centers. A further study was conducted to examine the effect on performance of the addition or deletion of 5 to 10 cluster centers in the majority class. The experimental results obtained using 44 small-scale and 2 large-scale data sets revealed that the clustering-based undersampling approach with the second strategy outperformed five state-of-the-art approaches. Specifically, this approach combined with a single multilayer perceptron classifier and C4.5 decision tree classifier ensembles delivered optimal performance over both small- and large-scale data sets.

分群式取樣法於類別不平衡問題之研究;Clustering-Based Under-sampling in Class Imbalanced Data

Instance selection in the supervised machine learning, often referred to as the data reduction, aims at deciding which instances from the training set should be retained for further use during the learning process Instance selection can result in increased capabilities and generalization properties of the learning model, shorter time of the learning process, or it can help in scaling up to large data sources The paper proposes a cluster-based instance selection approach with the learning process executed by the team of agents and discusses its four variants The basic assumption is that instance selection is carried out after the training data have been grouped into clusters To validate the proposed approach and to investigate the influence of the clustering method used on the quality of the classification, the computational experiment has been carried out

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Cluster-based instance selection for machine classification

The chapter proposes a middleware called JABAT (JADE-Based A-Team). JABAT allows to design and implement A-Team architectures for solving combinatorial and selected non-linear optimization problems. The JABAT is intended to become a first step towards next generation A-Teams which are fully Internet accessible, portable, scalable and in conformity with the FIPA standards. From the user point of view JABAT is the web-based application, in the paper refereed to as the e-JABAT.

e-JABAT – An Implementation of the Web-Based A-Team

The paper proposes a JADE-based A-Team environment as a middleware supporting the implementation and execution of population-based algorithms. The paper includes an overview of the JADE-based A-Team components and presents examples of the population-based architectures designed to obtain solutions to example combinatorial optimization problems including traveling salesman, resource-constrained project scheduling, vehicle routing and clustering problems. Conclusions focus on advantages of the JADE-based A-Team and on suggestions for further research

JADE-Based A-Team as a Tool for Implementing Population-Based Algorithms

The paper proposes a set of simple heuristic algorithms for instance reduction problem Proposed algorithms can be used to increase efficiency of supervised learning A reduced training set consisting of selected instances is used as an input for the machine-learning algorithm This may result in reducing time needed for learning or increasing learning quality or both The paper presents a collection of four algorithms, which are used to reduce the size of a training set The algorithms are based on calculating for each instance in the original training set the value of its similarity coefficient Values of the coefficient are used to group instances into clusters Out of each cluster only a limited number of instances is selected to form a reduced training set One of the proposed algorithms uses population-learning algorithm for selection of instances The approach has been validated by means of computational experiment

An Approach to Instance Reduction in Supervised Learning

The work deals with the distributed machine learning. Distributed learning from data is considered to be an important challenge faced by researchers and practice in the domain of the distributed data mining and distributed knowledge discovery from databases. Currently, learning from data is recognized as one of the most widely investigated paradigms of machine learning. At the same time it is perceived as a difficult and demanding computational problem. Even more complex and still to a large extent open is learning from the distributed data. One of the approaches suitable for learning from the geographically distributed data is to select from the local databases relevant local patterns, called also prototypes. Such prototypes are selected using some specialized data reduction methods. The dissertation contains an overview of the problem of learning classifiers from data, followed by a discussion of the distributed learning. The above includes the problem formulation and the state-of-the-art review. Next, data reduction, approaches, techniques and algorithms are discussed. The central part of the dissertation proposes an agent-based distributed learning framework. The idea is to carry-out data reduction in parallel in separate locations, employing specialized software agents. The process ends when locally selected prototypes are moved to a central site and merged into the global knowledge model. The following part of the work contains the results of an extensive computational experiment aiming at validation of the proposed approach. Finally, conclusions and suggestions for further research are formulated.

Ireneusz Czarnowski

Papers

Cluster-based instance selection for machine classification

e-JABAT – An Implementation of the Web-Based A-Team

JADE-Based A-Team as a Tool for Implementing Population-Based Algorithms

An Approach to Instance Reduction in Supervised Learning

Distributed learning with data reduction