By Lior Rokach, Oded Maimon
This can be the 1st finished ebook devoted totally to the sector of selection bushes in info mining and covers all elements of this crucial strategy. choice bushes became some of the most robust and renowned ways in wisdom discovery and knowledge mining, the technological know-how and expertise of exploring huge and intricate our bodies of knowledge for you to observe precious styles. the realm is of serious value since it allows modeling and information extraction from the abundance of knowledge on hand. either theoreticians and practitioners are regularly looking options to make the method extra effective, not pricey and actual. choice bushes, initially carried out in determination concept and records, are powerful instruments in different components similar to info mining, textual content mining, details extraction, computing device studying, and development recognition.This booklet invitations readers to discover the various advantages in info mining that call bushes supply: self-explanatory and simple to stick to while compacted; in a position to deal with quite a few enter information: nominal, numeric and textual; in a position to procedure datasets which could have blunders or lacking values; excessive predictive functionality for a comparatively small computational attempt; on hand in lots of info mining programs over quite a few structures; and, precious for varied initiatives, akin to class, regression, clustering and have choice.
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Extra info for Data Mining with Decision Trees: Theory and Applications
2 Hit Rate Curve The hit rate curve presents the hit ratio as a function of the quota size[An and Wang (2001)]. Hit rate is calculated by counting the actual positive labeled instances inside a determined quota. 10) j where t[k] represents the truly expected outcome of the instance located in the k’th position when the instances are sorted according to their conditional probability for “positive” by descending order. e. there is exactly one instance that can be located in this position) then t[k] is either 0 or 1 depending on the actual outcome of this speciﬁc instance.
Even ﬁnding the minimal equivalent decision tree for a given decision tree [Zantema and Bodlaender (2000)] or building the optimal decision tree from decision tables is known to be NP-hard [Naumov (1991)]. These results indicate that using optimal decision tree algorithms is feasible only in small problems. Consequently, heuristics methods are required for solving the problem. Roughly speaking, these methods can be divided into two groups: top-down and bottom-up with clear preference in the literature to the ﬁrst group.
However it is often important for the researcher to be able to inspect an induced classiﬁer. For such domains as medical diagnosis, users must understand how the system makes its decisions in order to be conﬁdent of the outcome. Since data mining can also play an important role in the process of scientiﬁc discovery, a system may discover salient features in the input data whose importance was not previously recognized. If the representations formed by the inducer are comprehensible, then these discoveries can be made accessible to human review [Hunter and Klein (1993)].