Discovering decision rules from numerical data streams

Abstract

This paper presents a scalable learning algorithm to classify numerical, low dimensionality, high-cardinality, time-changing data streams. Our approach, named SCALLOP, provides a set of decision rules on demand which improves its simplicity and helpfulness for the user. SCALLOP updates the knowledge model every time a new example is read, adding interesting rules and removing out-of-date rules. As the model is dynamic, it maintains the tendency of data. Experimental results with synthetic data streams show a good performance with respect to running time, accuracy and simplicity of the model.

Improving debt collection processes using rule-based decision engines: A case study of Capital One

“…The rules discovery process must be methodical and exhaustive, akin to the requirements gathering phase of the system development life cycle. Many researchers have studied and proposed algorithms for learning and discovering new and significant organizational rules (Apte, 1994;Ferrer-Troyano, 2004).…”

Incremental rule learning based on example nearness from numerical data streams

“…This paper describes an incremental learning algorithm that provides a set of decision rules induced from numerical data streams. Our proposal extends previous work [1] by filtering border examples that lie near to decision boundaries, so that every rule may retain a particular set of positive and negative examples. This information makes possible to ignore false alarms with respect to virtual drifts and avoid hasty modifications.…”

“…, y z } be the set of class labels. Let e i = ( − → x i , y i ) be the new i th training example arriving, where − → x i is a normalized vector in [0,1] m and y i is a discrete value in Y . A decision rule r is given by a set of m closed intervals [I jl , Iju] (j ∈ {1, .…”

“…Figure 1 shows an example in which two rules do not satisfy this condition using κ = 0.1. Since every example is previously normalized in [0,1] m , the divisor factor for the domain of each attribute is omitted in definition 1. When the first rule covering xi is founded -the resulting growth is therefore 0 -its support is increased by one unit and the index of the last covered example is updated as i.…”

A framework for on-demand classification of evolving data streams

“…The main thrust on data stream mining in the context of classification has been that of one-pass mining [8], [13], [16], [18], [23]. In reality, the nature of the underlying changes in the data stream can impose considerable challenges.…”