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Neural nets, with their flexible data representation capable of approximate any arbitrary function, are in a new renaissance with the development of Deep Leaners. These stack many layers (typically dozens of quasi-independent layers) of traditional neurons to achieve something very remarkable: self detection of important features from input data. The application of Deep Learning is not restricted to raw classification or regression; these family of techniques are applied to much broader fields such as machine vision and speech recognition. Recent advances went much further and combined a Deep Learning architecture with a Reinforcement Learning algorithm generating a computer program capable of beating classic Atari video games without being explicitly programmed for this task, scoring as a top human player. It is not surprising, therefore, that they appear in the last Nature’s cover (vol. 518, Num. 7540, pp. 456-568) . But how do they work? First, one must take a close look to the hum...

We live in a dynamic world where new technologies arise, dominate for a brief blink of an eye and then fall to oblivion rapidly. A part from the academia domain, it is interesting to check what are the current trends in the industry. Or even better: see the whole picture without discriminating. In this regard, a nice analytics tool is found in Google Trends, which allow us to track our terms of interest. The following chart shows the search interest for Machine Learning and Data Analytics . In a similar scale I depicted the search interest for Data Analytics and Big Data . As it is clearly noticeable, knowing both disciplines can open many doors in the industry. Finally, as top and also rising queries, Hadoop is the most popular term.

As in many real-world situations, union makes algorithms stronger. With this philosophy in mind, ensemble methods combine several weak classifiers into a massive one---in terms of accuracy. In the last post we learnt a primer with Random Forest . Therefore, the next cornerstone is gradient boosting. I mentioned Gradient Boosting many times in this blog, but I only commented the fundamental ideas, without discussing further the details. In this entry I will share my two cents. Let me introduce a little bit of history, first: recall the Kaggle-Higgs competition . The top scores in the leaderboard have been obtained by using distinct forms of gradient boosting, and XGBoost is the direct responsible of many of these. The question is, hence, how does this algorithm work ? Figure 1. A high-level description of the Gradient Boosting method I programmed. Click to enlarge. Informally, Gradient Boosting generates a sequence of classifiers in the form of an additive expansion, that i...

The Kaggle-Higgs competition has attracted my attention very much lately. In this particular challenge, the goal is to generate a predictive model out of a bunch of data taken from distinct LHC’s detectors. The numbers are the following: 250000 properly labeled instances, 30 real-valued features (containing missing values), and two classes, namely background { b } and signal { s }. Also, the site provides a test set consisting of 550000 unlabeled instances. There are nearly 1300 participants while I am writing this post, and a lot of distinct methods are put to the test to win the challenge. Very interestingly, ensemble methods are all in the head of the leaderboard, and the surprise is XGBoost , a gradient boosting method that makes use of binary trees.   After checking the computational horsepower of the XGBoost algorithm by myself, I decided to take a closer look at ensemble methods. To start with, I implemented a Random Forest , an algorithm that consists of many indep...

Several machine learning techniques foster the cooperation between subsolutions for obtaining an accurate outcome by combining many of them. Michigan-style LCSs are one of these families. However, the most well known are those that implement Boosting, and AdaBoost is the most successful of them (or, at least, the most studied one and the first to implement the ideas of Boosting). AdaBoost generates accurate predictions by combining several weak classifiers (also referred to as “the wisdom of the crowd”). The most outstanding fact about AdaBoost is that it is a deadly simple algorithm. The key of its success lies in the combination of many weak classifiers: these are very limited and their error rate is just slightly better than a random choice (hence their name). The typical implementation uses decision stumps (i.e., binary trees) that minimize the error between the prediction and the ground truth (that is, the desired outcome), so the classifiers have the form " if variable_...

An important challenge is learning from domains that do not have the same proportion of classes, that is, learning from problems that contain class imbalances (Orriols-Puig, 2008). Figure 1 shows a toy example of this issue. Notwithstanding, it is challenging because (1) in many real-world problems we cannot assume a balanced distribution of classes and (2) traditional machine learning algorithms cannot induce accurate models in such domains. Oftentimes it happens that the key knowledge to solve a problem that previously eluded solution is hidden in patterns that are rare . To tackle this issue, practitioners rely on re-sampling techniques, that is, algorithms that pre-process the data sets and either (1) add synthetic instances of the minority pattern to the original data or (2) eliminate instances from the majority class. The first type is called over-sampling and the later, under-sampling .  Figure 1. Our unbalanced data set. Black dots are the majority class (0) and ...

It is time to announce the Learning Classifier System Open Repository (LCSOR), a meeting point to foster the popularization of these exciting evolutionary computation techniques. In this repository I will upload distinct implementations of different Learning Classifier Systems. To start with, I uploaded a first version of the sUpervised Classifier System (UCS). Notice I created a page for this contend under the label LCSOR . I hope practitioners will enjoy this experience.