A major assumption in many machine learning and data mining algorithms is that the training and future data must be in the same feature space and have the same distribution. However, in many real-world applications, this assumption may not hold. For example, we sometimes have a classification task in one domain of interest, but we only have sufficient training data in another domain of interest, where the latter data may be in a different feature space or follow a different data distribution. In such cases, knowledge transfer, if done successfully, would greatly improve the performance of learning by avoiding much expensive data-labeling efforts. In recent years, transfer learning has emerged as a new learning framework to address this problem. This survey focuses on categorizing and reviewing the current progress on transfer learning for classification, regression, and clustering problems. In this survey, we discuss the relationship between transfer learning and other related machine learning techniques such as domain adaptation, multitask learning and sample selection bias, as well as covariate shift. We also explore some potential future issues in transfer learning research.

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A Survey on Transfer Learning

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

Probability Distributions.- Linear Models for Regression.- Linear Models for Classification.- Neural Networks.- Kernel Methods.- Sparse Kernel Machines.- Graphical Models.- Mixture Models and EM.- Approximate Inference.- Sampling Methods.- Continuous Latent Variables.- Sequential Data.- Combining Models.

Pattern Recognition and Machine Learning

We will review some of the major results in random graphs and some of the more challenging open problems. We will cover algorithmic and structural questions. We will touch on newer models, including those related to the WWW.

Random graphs

This paper presents the top 10 data mining algorithms identified by the IEEE International Conference on Data Mining (ICDM) in December 2006: C4.5, k-Means, SVM, Apriori, EM, PageRank, AdaBoost, kNN, Naive Bayes, and CART. These top 10 algorithms are among the most influential data mining algorithms in the research community. With each algorithm, we provide a description of the algorithm, discuss the impact of the algorithm, and review current and further research on the algorithm. These 10 algorithms cover classification, clustering, statistical learning, association analysis, and link mining, which are all among the most important topics in data mining research and development.

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Top 10 algorithms in data mining

This paper proposes Differential-Critic Generative Adversarial Network (DiCGAN) to learn the distribution of user-desired data when only partial instead of the entire dataset possesses the desired properties Existing approaches select the desired samples first and train regular GANs on the selected samples to derive the user-desired data distribution DiCGAN introduces a differential critic that can learn the preference direction from the pairwise preferences over the entire dataset The resultant critic would guide the generation of the desired data instead of the whole data Specifically, apart from the Wasserstein GAN loss, a ranking loss of the pairwise preferences is defined over the critic It endows the difference of critic values between each pair of samples with the pairwise preference relation The higher critic value indicates that the sample is preferred by the user Thus training the generative model for higher critic values would encourage generating the user-preferred samples Extensive experiments show that our DiCGAN can learn the user-desired data distributions

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Differential-Critic GAN: Generating What You Want by a Cue of Preferences

We propose a new model to refine image-to-image translation via an adversarial ranking process. In particular, we simultaneously train two modules: a generator that translates an input image to the desired image with smooth subtle changes with respect to some specific attributes; and a ranker that ranks rival preferences consisting of the input image and the desired image. Rival preferences refer to the adversarial ranking process: (1) the ranker thinks no difference between the desired image and the input image in terms of the desired attributes; (2) the generator fools the ranker to believe that the desired image changes the attributes over the input image as desired. Real image preferences are introduced to guide the ranker to rank image pairs regarding the interested attributes only. With an effective ranker, the generator would “win” the adversarial game by producing high-quality images that present desired changes over the attributes compared to the input image. The experiments demonstrate that our TRIP can generate high-fidelity images which exhibit smooth changes with the strength of the attributes.

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TRIP: Refining Image-to-Image Translation via Rival Preferences

In practical applications, machine learning algorithms are often needed to learn classifiers that optimize domain specific performance measures. In the past, the research has focused on learning the needed classifier in isolation, yet learning nonlinear classifier for nonlinear and nonsmooth performance measures is still hard. In this paper, rather than learning the needed classifier by optimizing the concerned performance measure directly, we propose a new framework ELperf to circumvent this problem. In ELperf, we first train auxiliary classifiers by optimizing an easy-to-handle performance measure; and then adapt these auxiliary classifiers to optimize the concerned performance measure. Under the function-level adaptation framework, the classifier adaptation problem of ELperf is formulated as a quadratic programming problem, which is similar to linear SVMperf and can be efficiently solved. Practically, by using nonlinear auxiliary classifiers, ELperf can generate nonlinear classifier that optimizes required performance measure, whilst keeping computational efficiency. In extensive empirical studies, we show that training auxiliary classifiers using accuracy is sufficiently good for ELperf, and other performance measure is not necessary. As well, it is shown that ELperf is effective and efficient in training classifiers that optimize performance measures, and even its classifier adaptation procedure is more efficient than linear SVMperf.

Efficiently Learning Nonlinear Classifiers for Domain Specific Performance Measures

Android piggybacked malware (i.e., apps that piggyback malicious code) are becoming ubiquitous in app stores. Malware writers often use obfuscation techniques to obfuscate piggybacked apps to evade detection by Android malware detectors. Previous studies in this field have focused on the impact of code obfuscations on the detection of piggybacked malware, but the impact of code deobfuscation on detecting obfuscated piggybacked apps has rarely been studied. Knowing about the impact of code deobfuscation can provide useful insights into obfuscated piggybacked apps and therefore the design of resilient Android malware detectors. In this paper we conduct an empirical study of code deobfuscations on detecting obfuscated Android piggybacked apps, focusing on three types of malware detectors: commercial anti-malware products, machine learning-based detectors, and similarity-based detectors. We observe that code deobfuscations can impact differently depending on the malware detectors. For example, some deobfuscation strategies can improve the precision of detecting obfuscated piggybacked apps. Also we observe that the examined deobfuscation tools (Simplify and Deguard) have a different impact on obfuscated piggybacked apps after deobfuscations.

An Empirical Study of Code Deobfuscations on Detecting Obfuscated Android Piggybacked Apps

Considering the special needs of credit risk analysis, the Infinite DEcision Agent ensemble Learning (IDEAL) system is proposed. In the first level of our model, we adopt soft margin boosting to overcome over fitting. In the second level, the RVM algorithm is revised for boosting so that different RVM agents can be generated from the updated instance space of the data. In the third level, the perceptron kernel is employed in RVM to generate infinite subagents. Our IDEAL system also shares some good properties, such as good generalization performance, immunity to over fitting and predicting the distance to default. According to the experimental results, our proposed system can achieve better performance in term of sensitivity, specificity and overall accuracy.

Ivor W. Tsang

Papers

Differential-Critic GAN: Generating What You Want by a Cue of Preferences

TRIP: Refining Image-to-Image Translation via Rival Preferences

Efficiently Learning Nonlinear Classifiers for Domain Specific Performance Measures

An Empirical Study of Code Deobfuscations on Detecting Obfuscated Android Piggybacked Apps

Infinite Decision Agent Ensemble Learning System for Credit Risk Analysis