I'm implementing a machine learning structure to try and predict fraud on financial systems like banks, etc... This means that there is a lot of different data that can be used to train the model eg. I'm having trouble deciding which structure is the best for this problem. I have some experience with decision trees but currently I have started to question if a neural network would be better for this kind of problem. Also if any other method would be best please feel free to enlighten me.

Conventional intrusion and detection methods to evaluate network anomalies have several impasses for large-scale datasets over ultra-blazing speed networks with disparate sources of data coming in with high-velocity and high-volume. Machine learning and artificial intelligence techniques mine the massive network datasets with IP size distribution can perform dichotomy of flow-based network traffic to diagnose the network anomalies as an effective solution. The simplex and similar size of the IP distribution with same attributes hitting the flow-based analysis on regular time intervals display the symptoms of network anomalies. Various flow-based monitoring tools such as nProbe and FlowMon Probe detect these intrusions on gigabit-sized networks. Two key detection techniques of NetFlow-based on large-scale and high-speed networks are: a) the misuse intrusion method; b) network anomaly detection method.

We propose using five data-driven community detection approaches from social networks to partition the label space for the task of multi-label classification as an alternative to random partitioning into equal subsets as performed by RAkELd: modularity-maximizing fastgreedy and leading eigenvector, infomap, walktrap and label propagation algorithms. We construct a label co-occurence graph (both weighted an unweighted versions) based on training data and perform community detection to partition the label set. We include Binary Relevance and Label Powerset classification methods for comparison. We use gini-index based Decision Trees as the base classifier. We compare educated approaches to label space divisions against random baselines on 12 benchmark data sets over five evaluation measures. We show that in almost all cases seven educated guess approaches are more likely to outperform RAkELd than otherwise in all measures, but Hamming Loss. We show that fastgreedy and walktrap community detection methods on weighted label co-occurence graphs are 85-92% more likely to yield better F1 scores than random partitioning. Infomap on the unweighted label co-occurence graphs is on average 90% of the times better than random paritioning in terms of Subset Accuracy and 89% when it comes to Jaccard similarity. Weighted fastgreedy is better on average than RAkELd when it comes to Hamming Loss.

Data mining has useful classification methods for the data analysis and prediction. One of them is decision tree induction, which is the learning of decision trees from the class-labeled dataset. It can provide an easy way to understand the data and view the relationship among attributes because it has a flowchart-like tree structure. When I applied the decision tree algorithm with parameters (criterion: gain_ratio and minimal gain: 0.03) to MSD dataset using the RapidMiner tool, the "start_of_fade_out" attribute is the best one to partition the data, as shown in Figure 1. Only 2 Rock and 1 New Age songs have start_of_fade_out that is greater than 547.698 seconds.

Whatever its name, it's the same thing – machines recognizing clinical problems in digital images ahead of the radiologists charged with making the diagnosis. The artificial intelligence (AI) trend is new, but it's gaining ground quickly, according to industry experts. The advent of these technologies and radiology's growing interest in and dependence on them has been discussed at national and international meetings, including the RSNA, HIMSS, and SIIM annual meetings, during the past year. But, there's still a long way to go. "We're just barely scratching the surface of using artificial intelligence in the last few years," said Eliot Siegel, MD, professor and vice chair of research information systems for the University of Maryland Department of Diagnostic Radiology and Nuclear Medicine. "There's an emergence of increasing interest in the largest companies in the world, including Google, Microsoft, Apple, and IBM, in actually starting to use these technologies for data extraction and evaluation."

I have been working on a c project using decision trees for some time. We have been using OpenCV for the DT part, but since the code of 3.1 seems to be riddled with issues and is partially inaccessible, I am wondering if an alternative, especially OpenDT (or maybe waffles) would be worthwile.

Last episode, we treated our Decision Tree as a blackbox. In this episode, we'll build one on a real dataset, add code to visualize it, and practice reading it - so you can see how it works under the hood. And hey -- I may have gone a little fast through some parts. Just let me know, I'll slow down. Also: we'll do a Q&A episode down the road, so if anything is unclear, just ask! Follow https://twitter.com/random_forests

The decision tree in the figure is just one of many decision tree structures you could create to solve the marketing problem. The task of finding the optimal decision tree is an intractable problem. For those of you who have taken an analysis of algorithms course, you no doubt recognize this term. For those of you who haven't had this pleasure (he says, gritting his teeth), essentially what this means is that as the amount of test data used to train the decision tree grows, the amount of time it takes to do so grows as well--exponentially. While it may be nearly impossible to find the smallest (or more fittingly, the shallowest) decision tree in a respectable amount of time, it is possible to find a decision tree that is "small enough" using special heuristics.

The standard approach of learning decision trees from histogram data is to treat the bins as independent variables. However, as the underlying dependencies among the bins might not be completely exploited by this approach, an algorithm has been proposed for learning decision trees from histogram data by considering all bins simultaneously while partitioning examples at each node of the tree. Although the algorithm has been demonstrated to improve predictive performance, its computational complexity has turned out to be a major bottleneck, in particular for histograms with a large number of bins. In this paper, we propose instead a sliding window approach to select subsets of the bins to be considered simultaneously while partitioning examples. This significantly reduces the number of possible splits to consider, allowing for substantially larger histograms to be handled. We also propose to evaluate the original bins independently, in addition to evaluating the subsets of bins when performing splits. This ensures that the information obtained by treating bins simultaneously is an additional gain compared to what is considered by the standard approach. Results of experiments on applying the new algorithm to both synthetic and real world datasets demonstrate positive results in terms of predictive performance without excessive computational cost.

Classically, for decision trees we define a split or various "buckets" to transform continuous data into discrete data. The data I am currently processing has uncertainty associated with it (each data point comes from an aggregate set). As such, I might define a boundary- let's say N, where a data's uncertainty could place it in multiple buckets (say the parameter value N? Normally these boundaries are binary, but I was considering using the probability of these'overlapping instances' towards both buckets weighted by their respective probabilities. This doesn't seem to violate the entropy term (total probability will still sum to 1). However, I can't place half an instance within a branch- which would destroy the meaning behind the term.