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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

An approach to the construction of classifiers from imbalanced datasets is described. A dataset is imbalanced if the classification categories are not approximately equally represented. Often real-world data sets are predominately composed of "normal" examples with only a small percentage of "abnormal" or "interesting" examples. It is also the case that the cost of misclassifying an abnormal (interesting) example as a normal example is often much higher than the cost of the reverse error. Under-sampling of the majority (normal) class has been proposed as a good means of increasing the sensitivity of a classifier to the minority class. This paper shows that a combination of our method of over-sampling the minority (abnormal) class and under-sampling the majority (normal) class can achieve better classifier performance (in ROC space) than only under-sampling the majority class. This paper also shows that a combination of our method of over-sampling the minority class and under-sampling the majority class can achieve better classifier performance (in ROC space) than varying the loss ratios in Ripper or class priors in Naive Bayes. Our method of over-sampling the minority class involves creating synthetic minority class examples. Experiments are performed using C4.5, Ripper and a Naive Bayes classifier. The method is evaluated using the area under the Receiver Operating Characteristic curve (AUC) and the ROC convex hull strategy.

/pdf/smote-synthetic-minority-over-sampling-technique-3s7jks70tk.pdf

SMOTE: Synthetic Minority Over-sampling Technique

Anomaly detection is an important problem that has been researched within diverse research areas and application domains. Many anomaly detection techniques have been specifically developed for certain application domains, while others are more generic. This survey tries to provide a structured and comprehensive overview of the research on anomaly detection. We have grouped existing techniques into different categories based on the underlying approach adopted by each technique. For each category we have identified key assumptions, which are used by the techniques to differentiate between normal and anomalous behavior. When applying a given technique to a particular domain, these assumptions can be used as guidelines to assess the effectiveness of the technique in that domain. For each category, we provide a basic anomaly detection technique, and then show how the different existing techniques in that category are variants of the basic technique. This template provides an easier and more succinct understanding of the techniques belonging to each category. Further, for each category, we identify the advantages and disadvantages of the techniques in that category. We also provide a discussion on the computational complexity of the techniques since it is an important issue in real application domains. We hope that this survey will provide a better understanding of the different directions in which research has been done on this topic, and how techniques developed in one area can be applied in domains for which they were not intended to begin with.

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Anomaly detection: A survey

Data Mining - Concepts and Techniques.

The goal of systems biology is to gain a more complete understanding of biological systems by viewing all of their components and the interactions between them simultaneously. Until recently, the most complete global view of a biological system was through the use of gene expression or protein-protein interaction data. With the increasing number of high-throughput technologies for measuring genomic, proteomic, and metabolomic data, scientists now have the opportunity to create complex network-based models for drug discovery, protein function annotation, and many other problems. Each technology used to measure a biological system inherently presents a limited view of the system. However, the combination of multiple technologies can provide a more complete picture. Much recent work has studied integrating these heterogeneous data types into single networks. Here we provide a survey of integrative network-based approaches to problems in systems biology. We focus on describing the variety of algorithms used in integrative network inference. Ultimately, the survey of current approaches leads us to the conclusion that there is an urgent need for a standard set of evaluation metrics and data sets in this field.

/pdf/a-survey-of-current-integrative-network-algorithms-for-36nu4d3d84.pdf

A Survey of Current Integrative Network Algorithms for Systems Biology

Many machine learning applications like finance, medicine, and risk management suffer from class imbalance: cases of interest occur rarely. Further complicating these applications is that the training and testing samples might differ significantly in their respective class distributions. Sampling has been shown to be a strong solution to imbalance and additionally offers a rich parameter space from which to select classifiers. This paper is concerned with the interaction between Probability Estimation Trees (PETs) [1], sampling, and performance metrics as testing distributions fluctuate substantially. A set of comprehensive analyses is presented, which anticipate classifier performance through a set of widely varying testing distributions.

/pdf/analyzing-pets-on-imbalanced-datasets-when-training-and-54h2xoq6pm.pdf

Analyzing PETs on imbalanced datasets when training and testing class distributions differ

Recipe recommendation systems play an important role in helping people find recipes that are of their interest and fit their eating habits. Unlike what has been developed for recommending recipes using content-based or collaborative filtering approaches, the relational information among users, recipes, and food items is less explored. In this paper, we leverage the relational information into recipe recommendation and propose a graph learning approach to solve it. In particular, we propose HGAT, a novel hierarchical graph attention network for recipe recommendation. The proposed model can capture user history behavior, recipe content, and relational information through several neural network modules, including type-specific transformation, node-level attention, and relation-level attention. We further introduce a ranking-based objective function to optimize the model. Thorough experiments demonstrate that HGAT outperforms numerous baseline methods.

/pdf/recipe-recommendation-with-hierarchical-graph-attention-21bu0cpi.pdf

Recipe Recommendation With Hierarchical Graph Attention Network

Given a project plan and the goal, can we predict the plan's success rate? The key challenge is to learn the feature vectors of billions of the plan's components for effective prediction. However, existing methods did not model the behavior outcomes but component proximities. In this work, we define a measurement of behavior outcomes, which forms a test tube-shaped region to represent "success", in a vector space. We propose a novel representation learning method to learn the embeddings of behavior components (including contexts, plans, and goals) by preserving the behavior outcome information. Experiments on real datasets show that our proposed method significantly improves the performance of goal prediction as well as context recommendation over the state-of-the-art.

https://dl.acm.org/doi/pdf/10.1145/3292500.3330867

TUBE: Embedding Behavior Outcomes for Predicting Success

Protein secondary structure prediction and high-throughput drug screen data mining are two important applications in bioinformatics. The data is represented in sparse feature spaces and can be unrepresentative of future data. Supervised learners in this context will display their inherent bias toward certain solutions, generally solutions that fit the training set well. In this paper, we first describe an ensemble approach using subsampling that scales well with dataset size. A sufficient number of ensemble members using subsamples of the data can yield a more accurate classifier than a single classifier using the entire dataset. Experiments on several datasets demonstrate the effectiveness of the approach. We report results from the KDD Cup 2001 drug discovery dataset in which our approach yields a higher weighted accuracy than the winning entry. We then extend our ensemble approach to create an over-generalized classifier for prediction by reducing the individual subsample size. The ensemble strategy using small subsamples has the effect of averaging over a wider range of hypotheses. We show that both protein secondary structure prediction and drug discovery prediction can be improved by the use of over-generalization, specifically through the use of ensembles of small subsamples.

/pdf/generalization-methods-in-bioinformatics-9niedlctsb.pdf

Nitesh V. Chawla

Papers

A Survey of Current Integrative Network Algorithms for Systems Biology

Analyzing PETs on imbalanced datasets when training and testing class distributions differ

Recipe Recommendation With Hierarchical Graph Attention Network

TUBE: Embedding Behavior Outcomes for Predicting Success

Generalization methods in bioinformatics