Over 150,000 new people in the United States are diagnosed with colorectal cancer each year. Nearly a third die from it (American Cancer Society). The only approved noninvasive diagnosis tools currently involve fecal blood count tests (FOBTs) or stool DNA tests. Fecal blood count tests take only five minutes and are available over the counter for as low as \$15. They are highly specific, yet not nearly as sensitive, yielding a high percentage (25%) of false negatives (Colon Cancer Alliance). Moreover, FOBT results are far too generalized, meaning that a positive result could mean much more than just colorectal cancer, and could just as easily mean hemorrhoids, anal fissure, proctitis, Crohn's disease, diverticulosis, ulcerative colitis, rectal ulcer, rectal prolapse, ischemic colitis, angiodysplasia, rectal trauma, proctitis from radiation therapy, and others. Stool DNA tests, the modern benchmark for CRC screening, have a much higher sensitivity and specificity, but also cost \$600, take two weeks to process, and are not for high-risk individuals or people with a history of polyps. To yield a cheap and effective CRC screening alternative, a unique ensemble-based classification algorithm is put in place that considers the FIT result, BMI, smoking history, and diabetic status of patients. This method is tested under ten-fold cross validation to have a .95 AUC, 92% specificity, 89% sensitivity, .88 F1, and 90% precision. Once clinically validated, this test promises to be cheaper, faster, and potentially more accurate when compared to a stool DNA test.

There exist two main approaches to automatically extract affective orientation: lexicon-based and corpus-based. In this work, we argue that these two methods are compatible and show that combining them can improve the accuracy of emotion classifiers. In particular, we introduce a novel variant of the Label Propagation algorithm that is tailored to distributed word representations, we apply batch gradient descent to accelerate the optimization of label propagation and to make the optimization feasible for large graphs, and we propose a reproducible method for emotion lexicon expansion. We conclude that label propagation can expand an emotion lexicon in a meaningful way and that the expanded emotion lexicon can be leveraged to improve the accuracy of an emotion classifier.

Word embeddings are representations of individual words of a text document in a vector space and they are often use- ful for performing natural language pro- cessing tasks. Current state of the art al- gorithms for learning word embeddings learn vector representations from large corpora of text documents in an unsu- pervised fashion. This paper introduces SWESA (Supervised Word Embeddings for Sentiment Analysis), an algorithm for sentiment analysis via word embeddings. SWESA leverages document label infor- mation to learn vector representations of words from a modest corpus of text doc- uments by solving an optimization prob- lem that minimizes a cost function with respect to both word embeddings as well as classification accuracy. Analysis re- veals that SWESA provides an efficient way of estimating the dimension of the word embeddings that are to be learned. Experiments on several real world data sets show that SWESA has superior per- formance when compared to previously suggested approaches to word embeddings and sentiment analysis tasks.

Machine learning algorithm K Nearest neighbors (k-NN) uses the principle of classifying data by using nearest neighbors. Nearest neighbors classifiers are defined by their characteristic of classifying unlabeled examples by assigning them the class of similar labeled examples. Despite the simplicity of this approach this method is extremely powerful and has been used for computer vision application, predictions and even, identifying patters in genetic data. The k-NN algorithm gets his name from the fact that uses information about the k-Nearest Neighbors to classify unlabeled examples. The letter is a variable term stating how many numbers of nearest neighbors will be used for the classification.

AI and machine learning algorithms are marketed as unbiased, objective tools. They are opaque mechanisms of bureaucracy and decisionmaking in which old-fashioned racist, sexist, and classist biases are hidden behind sophisticated technology, usually without a system of appeal. As their influence increases in society, we face a choice. Do we ignore their pernicious effects, or do we understand, regulate, and control the biases they exert? If we want them to represent transparent fairness, freedom, and consistency in an efficient, cost-saving manner, we must hold them accountable somehow.

We study the behavior of a fundamental tool in sparse statistical modeling --the best-subset selection procedure (aka "best-subsets"). Assuming that the underlying linear model is sparse, it is well known, both in theory and in practice, that the best-subsets procedure works extremely well in terms of several statistical metrics (prediction, estimation and variable selection) when the signal to noise ratio (SNR) is high. However, its performance degrades substantially when the SNR is low -- it is outperformed in predictive accuracy by continuous shrinkage methods, such as ridge regression and the Lasso. We explain why this behavior should not come as a surprise, and contend that the original version of the classical best-subsets procedure was, perhaps, not designed to be used in the low SNR regimes. We propose a close cousin of best-subsets, namely, its $\ell_{q}$-regularized version, for $q \in\{1, 2\}$, which (a) mitigates, to a large extent, the poor predictive performance of best-subsets in the low SNR regimes; (b) performs favorably and generally delivers a substantially sparser model when compared to the best predictive models available via ridge regression and the Lasso. Our estimator can be expressed as a solution to a mixed integer second order conic optimization problem and, hence, is amenable to modern computational tools from mathematical optimization. We explore the theoretical properties of the predictive capabilities of the proposed estimator and complement our findings via several numerical experiments.

Noisy PN learning is the problem of binary classification when training examples may be mislabeled (flipped) uniformly with noise rate rho1 for positive examples and rho0 for negative examples. We propose Rank Pruning (RP) to solve noisy PN learning and the open problem of estimating the noise rates, i.e. the fraction of wrong positive and negative labels. Unlike prior solutions, RP is time-efficient and general, requiring O(T) for any unrestricted choice of probabilistic classifier with T fitting time. We prove RP has consistent noise estimation and equivalent expected risk as learning with uncorrupted labels in ideal conditions, and derive closed-form solutions when conditions are non-ideal. RP achieves state-of-the-art noise estimation and F1, error, and AUC-PR for both MNIST and CIFAR datasets, regardless of the amount of noise and performs similarly impressively when a large portion of training examples are noise drawn from a third distribution. To highlight, RP with a CNN classifier can predict if an MNIST digit is a "one"or "not" with only 0.25% error, and 0.46 error across all digits, even when 50% of positive examples are mislabeled and 50% of observed positive labels are mislabeled negative examples.

We introduce canonical correlation forests (CCFs), a new decision tree ensemble method for classification and regression. Individual canonical correlation trees are binary decision trees with hyperplane splits based on local canonical correlation coefficients calculated during training. Unlike axis-aligned alternatives, the decision surfaces of CCFs are not restricted to the coordinate system of the inputs features and therefore more naturally represent data with correlated inputs. CCFs naturally accommodate multiple outputs, provide a similar computational complexity to random forests, and inherit their impressive robustness to the choice of input parameters. As part of the CCF training algorithm, we also introduce projection bootstrapping, a novel alternative to bagging for oblique decision tree ensembles which maintains use of the full dataset in selecting split points, often leading to improvements in predictive accuracy. Our experiments show that, even without parameter tuning, CCFs out-perform axis-aligned random forests and other state-of-the-art tree ensemble methods on both classification and regression problems, delivering both improved predictive accuracy and faster training times. We further show that they outperform all of the 179 classifiers considered in a recent extensive survey.

Welcome to the fourth article in a five-part series about machine learning. In this article, we will take a deeper dive into model evaluation and performance metrics, and potential prediction-related errors that one may encounter. Before digging deeper into model performance and error types, we must first discuss the concept of residuals and errors for regression, positive and negative classifications for classification problems, and in-sample versus out-of-sample measurements. Any reference to models, metrics, or errors computed with respect to the data used to train, validate, or tune a predictive model (i.e., data you have) is called in-sample. Conversely, reference to test data metrics and errors, or new data in general is called out-of-sample (i.e., data you don't have). Recall that regression involves predicting a continuous valued output (response) based on some set of input variables (features/predictors).

We consider the problem of efficient "on the fly" tuning of existing, or {\it legacy}, Artificial Intelligence (AI) systems. The legacy AI systems are allowed to be of arbitrary class, albeit the data they are using for computing interim or final decision responses should posses an underlying structure of a high-dimensional topological real vector space. The tuning method that we propose enables dealing with errors without the need to re-train the system. Instead of re-training a simple cascade of perceptron nodes is added to the legacy system. The added cascade modulates the AI legacy system's decisions. If applied repeatedly, the process results in a network of modulating rules "dressing up" and improving performance of existing AI systems. Mathematical rationale behind the method is based on the fundamental property of measure concentration in high dimensional spaces. The method is illustrated with an example of fine-tuning a deep convolutional network that has been pre-trained to detect pedestrians in images.